Research and analysis

Business data use and productivity study (wave 2): qualitative research report

Updated 28 January 2026

1. Executive summary

This report summarises insights from 40 qualitative interviews with UK businesses, undertaken by Ipsos and commissioned by the Department for Science, Innovation and Technology (DSIT). The research explores how businesses of varying sizes and sectors use data to enhance productivity, as well as the challenges associated with data use. When considering these findings, it is important to bear in mind that qualitative research is illustrative, detailed, and exploratory. Qualitative research cannot – and does not set out to be – representative of the wider population. We sampled participants purposively to highlight a diverse range of sectors and business characteristics.

1.1 Key data uses and benefits

Most businesses leverage various data types such as customer, operational, and external data while larger businesses tend to use more advanced categories such as geopolitical and sensor data. However, businesses that have been around for longer tended to use more traditional methods of data use and collection rather than innovative.

Most businesses agree that data use is seen as vital for operational efficiency, particularly in manufacturing and construction sectors, and for regulatory compliance in sectors like finance and healthcare. However this can lead to data use activities being dictated by regulatory bodies rather than engaged in proactively within sectors such as finance and healthcare.

Key benefits of data use include improved efficiency and productivity, data-driven decision-making, enhanced customer relationships, and opportunities for innovation and growth. These benefits are all related to cost savings and profit growth for businesses.

1.2 Challenges hindering data effectiveness

Data accuracy and consistency pose significant challenges to businesses, often stemming from manual data entry, outdated systems, and a lack of proactive data management. These hurdles can appear too large to overcome, preventing potential benefits of data use.

The transition from paper-based to digital data management remains a hurdle for some businesses, impacting data quality and efficiency.

Cyber security and data regulations are concerns, particularly for smaller businesses lacking resources and expertise, particularly for smaller businesses lacking resources and expertise to meet cybersecurity needs from data use and productivity.

A shortage of data analysis skills across various roles is a common barrier, and employee resistance to change, such as using more innovative tools, hinder effective data use.

1.3 Data culture and innovation:

A divide exists between businesses with traditional data cultures (non-digital focused), often viewing data as a supporting function, compared to those embracing data-driven approaches for innovation.

While data is primarily used for making processes more efficient, some businesses demonstrate advanced data use by developing new products and services, leading to business growth and new opportunities.

Strong leadership appears to be crucial in fostering a data-driven culture, with proactive leaders driving data use adoption and upskilling initiatives.

1.4 AI and government support:

AI adoption remains in its early stages and is primarily used for mundane tasks, though businesses recognise its potential for future applications.

In general businesses would like government support in addressing data skills gaps through targeted training programs or subsidies for data software.

Clearer guidance on GDPR requirements can alleviate challenges and promote responsible data use.

UK businesses recognise the important benefits of data use to improve productivity. However, addressing challenges related to data accuracy, skills gaps, cybersecurity, and regulatory compliance is crucial to unlock the full potential of data-driven growth. Government support in addressing these challenges will be instrumental in empowering businesses to thrive in an increasingly data-centric business environment.

2. Business context

2.1 Overview of businesses interviewed

Ipsos undertook 40 qualitative interviews on behalf of the Department for Science, Innovation and Technology (DSIT). These were follow-up interviews conducted with businesses who took part in the Data Use and Productivity Wave 2 quantitative survey. These businesses all agreed in the survey to be re-contacted for follow-up qualitative research.

Recruitment of qualitative participants aimed to include a range of business sizes and sectors, to understand a wide range of data use and the impact on business productivity.

This report compares interview analysis from small, medium and large businesses. Small businesses include both micro and small business sizes (1-49 employees). Medium businesses consist of 50-249 employees, and large businesses denote 250+ employees, including very large businesses (500+ employees). Sole traders were excluded from the qualitative interviews.

All main SIC sector categories of businesses were interviewed at least once, excluding public sector firms, that were also excluded from the quantitative survey. Due to the possibility of more advanced use and knowledge of data, businesses in the following sectors had a higher proportion of interviews:

• Finance and insurance

• Mining, energy and water

• Real estate

• Professional, scientific, technical

• Information and communication

To gather further varied responses, Ipsos aimed to recruit some businesses who identify as a data supplier. Ipsos also aimed to interview a spread of businesses that ranged from using simple, intermediate, and advanced data analysis and calculations.

When considering these findings, it is important to bear in mind that qualitative research is illustrative, detailed, and exploratory. Qualitative research cannot – and does not set out to be – representative of the wider population. We sampled participants purposively to highlight a diverse range of sectors and business characteristics.

Appendix A shows the breakdown of the achieved interviews by business size, sector, data supplier identity and level of data analysis.

2.2 Types of data used by businesses

While most businesses use a wide range of data, larger businesses tend to use data more strategically. Some sectors also highlight that certain data can be a priority for their business needs, thus becoming a priority.

One of the most prevalent types of data amongst all businesses is customer data. The level of detail ranges from basic personal details (names, addresses, phone numbers and emails) to more detailed customer data (purchase history, service usage and customer preferences). This type of data is important to businesses to use for marketing, sales or operational purposes.

“In the business we have customer data, which is everything from their identities, sometimes occasionally copies of passport or documentation, all the way to their transactions and their online use.”

Medium business, Finance and insurance sector

Operational data is another key data category for businesses. This can include financial data (sales, expenses, invoices), employee data (HR records, payroll) or core business processes (production data, project data). HR data in particular can include personal data, which businesses acknowledged need to be treated with caution. This data can be useful for more strategic opportunities such as sales forecasting and financial decisions, as well as core business function.

“We have weekly senior meetings where we review sales data and cost of goods data, margins, any kind of overhead that’s involved in the business. We spend a lot of time crunching those kinds of financial numbers.”

Medium business, Manufacturing sector

Many businesses use external data sources. This can include public sector data, industry reports, market research, and data from third-party providers. This is often linked to sales opportunities and competitiveness in business sectors.

“We also subscribe to a number of journals and business websites and things where we gather data. Our procurement team, they’re speaking to our suppliers on a regular basis to understand what’s happening in the cocoa market or the sugar market.”

Medium business, Manufacturing sector

Larger businesses tend to use more advanced data categories, such as geopolitical data to assess risk, or sensor data that are specific to their industry. While data can be used for operational purposes, others use data for strategic purposes. For example, an agriculture firm uses public sector data to forecast productivity. A logistics company also leverages data in planning a business expansion. This highlights that data use for strategy as well as efficiency.

Some data types are more important to certain sectors and industries. This has important implications in how these businesses may use or analyse data. For example, businesses in the financial sector tend to mention data usage and regulatory compliance. Manufacturing firms tend to talk about data in terms of production processes, inventory, timesheet and quality control. Construction firms cite project management and health and safety use for data. Real estate firms focus on customer relationship management and market analysis as key uses for different data types. Some businesses also mentioned monitoring fuel usage. These different priorities for certain sectors can lead to specific use cases of data.

2.3 Importance of data use to the business

Businesses either view data use as a vital part of the business, or more of a supporting function. Of those who view data as vital, they often frame the use of data as improving operational efficiency. This is especially common in the manufacturing and construction sectors.

“The whole way that the plant operates is on feedback loops… if you lose the connectivity between one part of the plant and the other, it just goes on blind.”

Medium business, Mining, energy and water sector

Heavily regulated sectors, like finance and insurance, healthcare and education tend to note compliance data as vital to the business. These regulators can have a major impact on the types of data these businesses collect, store or analyse.

“You’re kind of guided by the regulator in terms of what data you hold and what you need to hold. So if their requirements change, that might lead to you to do something different.”

Medium Business, Human, health and social work sector

Customer Relationship Management systems (CRMs) are commonly referred to as vital to businesses who use them. Businesses often state that CRMs are helpful with automation and management, positioned in terms of customer relationships to drive sales. CRMs are used in varied sectors, such as Finance or Real Estate.

“We have our own CRM system which is pulling together what drinks they like, how they have their tea. We’re, we’re trying to build that out to try and hit as many kind of personable touches.”

Small business, Finance and insurance sector

Some businesses use data as a product in and of itself, directly leading to sales.

“…we actually build our own visual analysis tool. So rather than using off-the-shelf data analysis, we would do that ourselves and produce a front end for charts and so on to view that data.”

Micro/Small business, Information and communication section

“We host it all and then we present over the Internet the applications for them to use… so the benefit for them is they don’t need to have their own internal IT people looking after the database.”

Micro/Small business, Information and communication section

The challenges of data are at the forefront of business’ minds when discussing data as a supporting role rather than vital role. These challenges include lack of data culture, outdated technology and legacy systems, concerns about data security, and privacy regulations. Paper versus digital data management is a common theme, despite the overall shift towards digital. The sheer volume of data can also overwhelm businesses and prevent them from effectively managing their data.

“And that is where the culture comes in. So, some of us are ahead. Whereas others are maybe a bit behind. And the finance department … I still hear staplers, paper, staplers, paper.”

Large business, Mining, energy and water sector

Some of these guys are in their 50s and they don’t really use a computer. They don’t really use LinkedIn.”

Large business, Mining, energy and water sector

However, businesses who view data as more of a supporting role, tend to think this might change in the future. This is because of a culture towards data-driven insight, development of new technologies such as AI, competitive pressures, financial monitoring in uncertain economic conditions, and leadership communications.

3. Data benefits and challenges

3.1 Benefits of data use

Most businesses see data use as providing important benefits to their business. The most common benefits businesses note are improved efficiency and productivity, data-driven decisions, customer relationships and innovation and growth.

Efficiency and productivity increases are commonly perceived benefits of data use. Increased operational efficiency and productivity is achieved through activities such as accurate tracking of customer demands, maintaining optimal stock levels, automating payroll processing, and managing client relationships. One manufacturing company describes how real-time job tracking allows them to provide customers with up-to-date information on order status and forecast stock supply.

“That enables us to be able to know where any job is in any one point and where it is in the factory. So if a customer phones up and says can you give me an update on this job? We’re able to go onto the ERP system and tell them straight away…”

Medium business, Manufacturing sector

“We work with our sales teams who in turn work with their customers to forecast products to determine how much of each product we need to make or purchase. We use that data to influence our purchasing decisions and contracting decisions.”

Medium business, Manufacturing sector

A fruit farm used data to establish benchmarks for different risk profiles, ensuring consistency and fairness in their pricing and underwriting decisions. One company leveraged data to establish benchmarks for different risk profiles, ensuring consistency and fairness in their pricing and underwriting decisions.

“Basically we can run various reports off that so we can see if people have done site visits or like number of postings we’ve done, so how many times we’ve communicated with our leaseholders.”

Large business, Real estate sector

Businesses are using data analysis to support strategic decision-making. This often includes measuring Return on Investment (ROI) and performance management. Businesses framed the risk of not using data informed decisions is that they are based on assumptions, rather than evidence.

“We’ve had a few different assignments on what was needed on the budget and I’ve seen from sales and marketing perspective what numbers are working in terms of ROI based on the booking.”

Large business, Food or hospitality sector

“If we don’t use data, there’s a lot of assumptions that are made and then you’re not necessarily making good decisions that are going to actually do what you think they’re going to do.”

Medium business, Arts, entertainment and recreation sector

Businesses can gain reputational benefit from being data-lead. A mining, energy, and water company described their use of data to improve client trust by providing evidence for their services, while a software development company spoke about increasing project transparency to clients through automated reporting.

Data-driven insights can also contribute to better client or customer relationships. For example, a manufacturing business explains how using data strengthens their ability to respond to customer needs and negotiate prices based on evidence, underpinning their reputation for trustworthiness in materials science.

“We put together trends and insight reports that we share with our customers about the industry. …And so we try and stay on top of that type of innovation and communicate it to our customers and hopefully that proves pretty well that they take up some of our suggestions.”

Medium business, Manufacturing sector

Some businesses highlight the potential for innovation and business growth through the effective use of data. A small professional company uses data to develop apps, which has directly led to repeat business.

“So the apps were one [benefit], we’ve done two… we get repeat business when we do things well.”

Small business, Professional, scientific and technical sector

Businesses discuss innovative data in terms of new products and services. However, this is often framed in terms of improving processes and efficiencies rather than creating completely new products.

“Now we’re much more focused on creating new avenues on growth. We look at those [new product launches] in a meeting on a monthly basis to see how those are performing against the forecast that was provided by the sales team or by the customer to measure that impact. So it’s less about introducing new products and more about helping us to kind of refine what we’re going to do going forward.”

Medium business, Manufacturing sector

Data use helps companies meet legal obligations and ensure business integrity, particularly in the financial services sector. Data management is crucial for maintaining legal compliance, avoiding significant penalties, and reputational damage.

The real estate sector highlights the potential of data analysis in enabling them to react quickly to market fluctuations and adapt their strategies in response. One wholesale and retail company use data to improve its environmental impact, through better management of fuel. A different business in the agriculture, forestry, and fishing sector notes an environmental benefit of data use through better management of their power grid capacity.

3.2 Challenges of data use

While there are evidently benefits of data use and productivity within UK businesses, there are also important challenges to overcome.

The biggest challenges businesses face are data accuracy and consistency. This is a theme across all business size and sectors interviewed. Manual data entry appears to be one of the key sources of inaccurate or inconsistent data. Businesses are concerned about human error impacting data entry tasks, which are not quality checked. Outdated data, lack of individual motivation for quality assurance, and a reactive rather than proactive data management mindset are other key challenges for businesses using data to be productive.

“It’s badly structured, it’s poor quality. If people don’t know what they’re going to use the data for, they don’t care about the quality of what they put it in. If they don’t see the output, they don’t care about the quality of the input because there’s no causal link to it.”

Small business, Mining, energy and water sector

“They might not necessarily ever need updating and they might be a resident who’s with us for 10 years and their next of kin move nine times in those years. But it’s updated when it’s needed, to be sure.”

Medium business, Health, social care or social work sector

Another common theme is paper versus digital data management. Some businesses state they use writing on paper as a way of entering customer’s details, which can lead to errors due to paper-specific issues such as handwriting.

“Because the data is only as good as what’s put in, if you can’t read someone’s handwriting on their starter forms, then we might be putting incorrect data in and it never gets changed. Or if they don’t tell us that they’ve moved, which is quite often.”

Medium business, Administration sector

“Quite old school. Filling in things like on paper and some people’s handwriting. Even though they could pull it in online, they seem to like writing it down.”

Small business, Real estate sector

Some businesses also stated that data structure and formatting can be an issue. This occurs when data from diverse sources require extra data processing which could lead to errors. Specifically, larger businesses note that using multiple systems of data can easily lead to errors or data duplication. System integration is not a unique challenge to larger firms, smaller businesses also describe issues of data silos when using suppliers or third parties.

“We probably use about 15 to 20 different systems across the departments. And within that, some of these systems don’t talk to each other… Not all of them do integrate… the main issue is probably going to be the integration with the different systems really.”

Large business, Hotel and catering sector

“I suppose when you’re using a third party, if you want to change from one supplier to another, there are challenges around that. Getting data from a supplier’s database, extracting it and reinstalling it into a new piece of software would be time consuming. So you lose a little bit of that flexibility if you’re outsourcing to other suppliers.”

Small business, Information and communication sector

“The biggest problems we’ve had or the slowdowns we’ve had is when software don’t integrate. We’ve got our marketing database which doesn’t integrate to our CRM, which means that suddenly then we’re going to have to duplicate everything and that leaves room for error if it’s human.”

Small business, Professional, scientific and technical sector

A further concern for businesses using data to be more productive is cyber security and data regulations. Businesses mentioned cyber security is important, but sometimes not accessible, regardless of business size. There is a sense that cyber security threats are growing, and this is a concern when handling a large amount of data.

“Multifactor authentication is fine if everybody’s got a phone. Well, actually not all our workers have that ability… Sometimes it can be a bit of a sledgehammer to crack a nut.”

Large business, Administrative and support service sector

“All our backup goes to the cloud now. But due to reasons for cyber security, we also have to keep hard backup drives as well in a safe on site.”

Small business, Finance and insurance sector

When businesses talk about cyber security threats, they often discuss data regulation, specifically GDPR compliance. Businesses perceive GDPR as a potentially limiting factor in data driven processes or products. Small businesses in particular note the large amount of resource needed to ensure any new systems or uses of data are compliant. The perceived difficulty of achieving GDPR compliance can also be seen internally, as staff can be mistrusting of how the business handles and secures their personal data.

“All these little things, they were the main challenges I would say being able to, to meet the requirements of those new GDPR rules that came in on a system which for us, because we’re very niche, was self-built. It’s something that we have to change our system for each time and rely on it to be able to, to work and keep it up to date, to do what it needs to do.”

Small business, Finance and insurance sector

“One of the biggest issues with the transition was how our workforce perceive things. So we moved from a traditional payroll sort of structure to a cloud-based system and a lot of them had concerns that how we were going to manipulate their data and store it wasn’t particularly secure or sort of baseline levels concern like that.”

Large business, Transport and storage sector

A common challenge amongst participants is a lack of data analysis skills. Staff with less data analysis capabilities can see the benefit of data use and productivity, but they are not able to act on this due a lack of knowledge and experience in data management and analysis. Smaller businesses note the importance of recruiting individuals who are experienced in data analysis in the future to combat this challenge. Amongst staff, smaller businesses tend to demonstrate a level playing field with data analysis skills and literacy, whereby most staff have the same skill level. However, it appears in medium and larger businesses that data skills and processes can often be separated into specific teams, which leads to a silo of skills. This can cause issues with data use and skill development for individuals in larger businesses.

“I don’t actually do any data analysis per se. We don’t have any really good tools for that. Google website stats doesn’t really tell you too much. That may be because we haven’t set it up properly, but I’m not sure what it would tell me.”

Small business, Real estate sector

“We’ve got some in house skills, but we are certainly looking at potentially data modelers going forward when we start to do something a bit more sophisticated which might link into GIS graphical systems in terms of representing the data in a geographic context.”

Small business, Professional, scientific and technical sector

“Our next recruitment has got to be someone with more data skills and data modelling skills and a data analyst because we are part timers at it very much so… but I think we recognise that our skill set is going to have to change in the business.”

Small business, Professional, scientific and technical sector

Other businesses suggest that employees can be resistant to the change needed for effective data use. Participants note the difficulty of ‘holding their hands’ when implementing new data management systems in an increasingly technologically advanced environment. One participant from a large business in the construction sector notes that they do not have an intranet or common central area for information, showing that some sectors may be more behind than others regardless of business size.

“I think there’s certainly among the staff, there’s always a bit of scepticism about [data use], but generally I think all senior management are on the same wavelength…”

Large business, Real estate sector

“Getting people onto spreadsheets and out of pen and paper was very hard. And now getting people who are comfortable with spreadsheets or starting to use apps, you’ve really got to hold their hands.”

Large business, Agriculture, forestry and fishing sector

“This is a [large] firm that lacks an intranet. So there’s no common central area for information.”

Large business, Mining, energy and water sector

Training staff was another challenge presented to businesses. Businesses spoke about the lack of resource to train staff, or a lack of motivation for staff to undertake learning and action proactively. In particular, cost was a common issue around training and data use. A participant from a large business in the administrative sector notes that they learned a lot about data management from this proactive approach, watching YouTube videos rather than any formalised data management training.

“If we, if I had unlimited or not even unlimited, but just a bigger training budget, then maybe I could send my staff on training once a month until they get it.”

Micro/Small business, Other service activities sector

“People still taking registers on paper because they just find it slows them down. They can’t get going with their classes for the first 20 minutes because they’re trying to fight with this database they don’t understand how to use, despite the amount of training sessions.”

Micro/Small business, Education sector

“Our knowledge was sort of developed by watching YouTube videos, rather than having an inherent experience or professional training.”

Large business, Administrative and support service sector

3.3 Data sharing or supplying

When prompted, UK businesses rarely view themselves as a data supplier. This can occur even when the business describes elements of sharing or selling data to clients or third parties. It seems businesses tend to view data sharing or selling in a more conservative manner. Specifically, there appears to be some concern about data regulations and risks by identifying as a data supplier/sharer.

“We don’t share the data. We develop reports based on the data…Then supplying to your clients which have a professional opinion added to them, I guess you’d say, in a risk assessment.”

Small business, Professional, scientific and technical sector

Some businesses when asked deferred to GDPR or data protection processes or regulatory compliance around sharing data. This is evident in smaller businesses, or those in the finance and insurance, education and construction sectors. If regulatory compliance feels too restrictive, this could have important implications in the effectiveness of how businesses use data for improved productivity.

“All data really. It’s only made available to the people who need to see it.”

Small business, Information and communication sector

“We will have to share with our clients’ findings. So for example, the data we gather on, let’s say, example, site setups, when we set up a site on a road. We audit that. We have a set number of audits. We have to do so that is shared with the client.”

Medium Business, Construction sector

However, some businesses do share or sell data, sometimes in innovative ways. One medium business describes their ability to rent data from another business to use for marketing purposes.

“So for instance, say for exhibitions, if we want to encourage more visitors to come, we will ask an external company to find lists of people and phone them up and explain the exhibition and sort of encourage them to come and so that those are the two main ones really… we’re just renting it, it’s their data, it’s not ours. So we can’t just copy and paste it.”

Medium business, Information and communication sector

3.4 Use of public sector data

Some businesses use public sector data, mostly to help improve their competitiveness or market positioning. For example, some businesses use public procurement databases to identify potential clients. Of the businesses interviewed, the real estate, mining and professional, scientific or technical firms in particular tend to use public sector data.

“So we’ve got a land of new homes team who look at planning permissions on website, on government websites.”

Small Business, Real estate sector

“We do use things like land designations, which comes out of things like land registry, but not really local authority data.”

Micro/Small business, Professional, scientific and technical sector

One non-profit organisation uses public sector data to understand local demographics and needs, and support fundraising efforts. However, in general, public sector data tends to be viewed as supplementary data rather than crucial to businesses.

“We use ONS  … industry reports and things like market reports… general consultancy services.”

Medium business, Professional, scientific and technical sector

4. Data culture and innovation

4.1 Influence of data use on company culture

Businesses that align with a sense of tradition, such as non-digital processes, tend to view data as an aid rather than a central aspect of the business. However, businesses that have a more innovative culture, such as an apparent technologically entrepreneurial spirit, see the benefits of data-driven culture or upskilling of staff.

“We are fairly traditional in terms of the way we operate our business.”

Medium business, Manufacturing sector

“A lot of the staff don’t have great IT skills and they’re not used to working in an office environment.”

Micro/Small business, Other service activities sector

Again, skills gaps are a common challenge that prevent a culture of data-driven insight or advanced use of data in some businesses. Some sectors like manufacturing or construction seem to be more aligned with traditional data company culture. The length of time that the business has been running appears to be a key determinant of tradition or innovation. According to the UK Innovation Survey 2020-2022, over 20% of businesses believe that lack of qualified personnel is a highly important barrier to innovation, which has increased since the previous two iterations. This could mean that perhaps companies that have been around longer are not investing in personnel to adapt and be innovative.

“It’s a cultural shift. It’s ever improving because those people then come and ask for not necessarily more, but more specific, more intelligent, more targeted information, which is a much better way of going of can I have this?”

Large business, Agricultural sector

“There’s one or two people that have really stood up and excelled at getting involved in some of the data stuff…there’s definitely been some upskilling off the back of it.”

Micro/Small business, Professional, scientific and technical sector

4.2 New products or services emerged from data use

Businesses tend to view data use as largely improving process efficiencies rather than introducing specific new products, services or processes. These improvements are often tied to the use of specific data management tools such as a CRM, or AI tools.

“It’s made things a little bit more efficient, obviously, especially consolidating data across multiple offices. The culture has benefited from that, from not doing so many repetitive tasks.”

Medium business, Professional, scientific and technical sector

“So AI is now actually reading invoices and bills from suppliers. It’s processing it, it’s putting it on the system, it’s putting it into the right nominal code, it’s doing all of the tidy up work.”

Small Business, Professional, scientific and technical sector

As mentioned, businesses seem to be apprehensive about new innovations and therefore new products. This could be linked to challenges noted earlier around a lack of data skills and confidence, resource, or regulation concerns. This suggests that there is scope for new data-driven products and services, but the fear and challenges around data use need to be addressed first.

However, some businesses show advanced data-driven use, through new products and services. One business in the human, health and social work sector developed an application used for medical diagnosis. A fruit farm uses data to determine strains of certain fruits to improve productivity and forecasting. When businesses develop and implement these new products or services, they tend to state they have a beneficial impact for the business.

4.3 Leadership and data use

Leaders tend to play a key role in promoting data-driven culture in businesses. This is especially common amongst larger businesses. However, some leaders who may be towards a later stage in their career can seem more hesitant to make data-driven changes within businesses. Directors and senior staff seem to be more data driven in medium to large business sizes compared to smaller businesses.

“It’s sort of day to day for us to utilise data effectively.”

Medium business, Professional, scientific and technical sector

However, while data is notably valuable for operational efficiency and customer service, there is a lack of a proactive data culture. Leaders across sectors such as mining, energy and water, human,  health and social work, and education sectors tend to have a reactive approach.

“I don’t think we need policies as such. If it was a bigger organisation with multiple people working on it we would as a company need a policy.”

Small business, Mining, energy and water sector

Data use appears to be higher when leaders have a hands-on approach, seeking external guidance, developing bespoke training platforms for staff, and emphasising the importance of data-driven decisions. This reliance on individual leaders to promote a positive data culture could put businesses at risk if these individuals leave the business.

4.4 Use of AI based technologies

Some businesses mention AI based technologies to help with data use, while others appear to have limited knowledge and use. Participants in the information and communication sector seem more likely to use AI tools compared to other sectors that were interviewed. In general, AI tends to be used sporadically and for mundane tasks.

“Only very sporadic use, primarily I know some colleagues have used it for writing emails or proposals and so that’s just very limited use. I myself fall into that camp. I’ve used it a couple of times when I wanted to write an email or a letter to somebody. I just needed a little bit of help with it. So almost as experimentation as much as anything else.”

Medium business, Manufacturing sector

“We use it to do sort of mundane tasks of, I’ve got this data set. I need it to be manipulated. An example was we were putting a presentation together where we had to put 120 names on a PowerPoint slide and each name had to have their job title right aligned underneath them.”

Small business, Information and communication sector

Overall, businesses tend to view the use of AI as something their business may look at in the near future, if not already currently using. It does not appear to be central to any data processes for businesses at the time of the interviews. This is consistent with BICS survey data, that businesses are increasingly using or are expecting to use AI more in the future.

5. Data collection and analysis methods

5.1 Internal data collection and management

While there are a wide range of ways businesses collect data internally, there are common threads amongst businesses of all size and sector.

CRMs are especially top of mind when discussing internal data collection. These are often cited as vital parts of the sales and marketing elements within a business. CRMs seem to be particularly useful for information and communication sector participants.

“We have an online form which populates Salesforce and that’s where we get the registration data from. And then when they sign into an activity, they’ll probably do it on paper because they can’t get us started.”

Small business, Other services activities sector

In general, larger businesses indicate use of automation in digital systems. This is particularly evident in the mining, energy and transport and storage sectors. For example, some businesses use data software such as SQL or proprietary software.

Smaller businesses are more reliant on manual data collection processes, such as for potential customer information, stocktaking and sales data entry. There is also a digital divide, with a few smaller firms still relying on paper-based data collection which can risk inefficiencies. Still, amongst most businesses, Microsoft Excel is the most common and seemingly preferred internal data collection and analysis tool.

“Nothing grander than Microsoft Excel at the moment.”

Small business, Professional, scientific and technical sector

“Obviously because a lot of what we do is manual and a lot of it’s on spreadsheets which can often be linked to pivot tables and things like that. An error in one portion of the table can generate errors downstream.”

Medium business, Manufacturing sector

Third-party services and cloud-based platforms are leveraged by some businesses to manage specific functions such as HR and data storage, offering benefits like scalability and access to expertise.

5.2 External data collection and management

External data collection methods seem less common compared to internal, but remain prevalent amongst businesses, nonetheless.

Subscription services are a common way of collecting external data. This is often used for business development or financial data in administrative, financial or professional sectors. Other businesses collect data directly from clients, such as in the real estate, professional and administrative sectors. As mentioned, some businesses also seek external data from the public sector to use as databases for marketing purposes.

“One thing we might buy are utility network records.”

Small business, Administrative and support service sector

“We do use external research to bolster knowledge and understand what’s happening”.

Medium business, Arts, entertainment and recreation sector

Some businesses in real estate and information and communication sectors use informal contacts and networking as sources of external data collection.

5.3 Data analysis capabilities

As mentioned, most businesses mention the use of Microsoft Excel. Businesses do acknowledge that this analysis can be manual in Excel, but that data is often processed and managed in Excel which leads to analysis in the same way.

“The transfer mechanism for those things is an Excel model of some sort.”

Small business, Mining, energy and water sector

“They primarily do everything through Excel. It’s a pretty manual process. Obviously, you can manipulate the data in Excel in different ways like with pivot tables.”

Medium business, Manufacturing sector

Tracking sales is identified as a key reason for data analysis amongst all business sizes and sectors.

“Keeping track of sales figures and purchases and making sure that we’re in line with previous periods and previous months, highlighting any issues within those and correcting them.”

Large business, Arts, entertainment and recreation sector

Some businesses move away from Excel due to the large quantity of data and the sophistication of analysis. Siloed data teams are also commonplace in some businesses, which allows for more sophisticated data analysis but runs the risk of increasing data skills gap among staff. Externally, some businesses use consultants or other third-parties to analyse data for them.

5.4 Staff roles involved with data use

Most businesses have staff that use data in some capacity however the specific uses of data does vary depending on the staff role. For example, financial teams tend to look at financial reporting and analysis. Sales teams often look at CRMs to analyse customer data. One business notes the difficulty of siloed IT staff in terms of data analysis and skills.

“The other challenge we have is that some of those more technical skills currently sit in our IT department. I’m trying to ensure through working with the head of IT, we agree the remit of our respective teams so that essentially data engineering would not sit within the IT function. Data analysts would sit in my function.”

Large business, Administrative and support service sector

Real estate, professional and scientific sectors tend to identify dedicated data management and maintenance. However, it is important to note these roles appear to involve simpler data management tasks, rather than more sophisticated analysis.

“We have the need to assign tasks to and schedule them so that we can manage that transactional workflow.”

Large business, Administrative and support service sector

Larger businesses tend to have a more centralised data analysis structure, where individuals in specific roles handle most, if not all, data analysis tasks. However, this structure can create potential bottlenecks due to data access. This can limit the ability of other staff to leverage data effectively in their roles.

“The only person in the business who has access to every file is me. Other people will only have access to, let’s say, the contract they’re working on.”

Medium Business, Construction sector

“We prevent anything from going wrong by just not allowing people to see it. That’s what’s the best way of doing it. And on our main accounting and purchase order system, again, there’s authority levels.”

Medium Business, Construction sector

Depending on the size of the organisation, managing directors often take on higher level data analysis tasks such as financial monitoring and identifying business opportunities.

5.5 Sharing of data analysis

Businesses show a varied range of data analysis sharing practices, with a noticeable divide between internal and external sharing of analysis. Informal communication channels are commonly used to share data analysis, especially in small businesses within the information and communication sector. Some smaller businesses indicate preference for more formalised structures and processes for data analysis dissemination that larger businesses possess.

In these larger businesses, analysis results are commonly shared through direct reports to senior management and monthly. In sectors such as Manufacturing, Agriculture, Forestry and Fishing, Construction and Mining, and Energy and Water, regular meetings and reports form the backbone of data analysis sharing.

“We have annual meetings and quarterly meetings and yes, some of that analysis is shared across the business.”

Medium business, Professional, scientific and technical sector

Compared to sharing of data in general, sharing data analysis did not appear to be as much of a concern for businesses. The only hesitation around sharing data analysis internally or externally appears to be the risks of confidentiality. However, external clients and customers often receive reports which includes data analysis. This analysis and report sharing is often a part of their client contract or expectation of work.

Many businesses use data visualisations and dashboards for internal analysis sharing while others use automated analysis updates. Again, challenges of data literacy and skills means that sharing of analysis can be siloed.

6. Challenges and government support

6.1 Further challenges and government support

Businesses view data use as an area that is both challenging yet beneficial, with a potential for more effective use which could lead to productivity gains, if challenges are addressed. In general, businesses would like to see initiatives from the government aimed at addressing data skills gaps. This is a common point of interest across all business sizes and sectors.

Smaller businesses would like the government to help reduce financial burdens of data use challenges. This could take shape in the form of targeted and government funded data training. Some businesses note that the government could provide and subsidise sophisticated data management systems, to increase accessibility.

Some businesses mention sector specific requests from the government. Participants in the finance and insurance sector seem more concerned about AI and the ethical implications that government needs to address. Some businesses across sectors also desire clearer guidance on GDPR requirements, to alleviate current challenges or fear of negative consequences from data misuse.

7. Appendix A: Participant sample

Table 1: Achieved qualitative interviews by size, sector, data supplier business, and data related skills

Business size	Achieved
Micro/small (1 to 49 employees)	14
Medium (50 to 249 employees)	16
Large (250+ employees)	10
Data supplier	Achieved
Able to answer depth questions on data supply	11
Business is involved in the production and delivery of data-driven products and services? (e.g. selling data)	12
Sector	Achieved
A = Agriculture, forestry and fishing	1
B D E = Mining, energy and water	4
C = Manufacturing	2
F = Construction	1
G = Wholesale and retail, repair of motor vehicles	1
H = Transport and storage	2
I = Hotel / catering	1
J = Information and communication	5
K = Finance and insurance	4
L = Real estate	4
M = Professional, scientific, technical	6
N = Administrative and support service	4
P = Education	1
R = Arts, entertainment and recreation	1
Q = Human, health and social work	2
S T U = Other service activities	1
Data related skills	Achieved
Simple basic data entry or calculations “Between 1% and 100% of the roles”	40
Intermediate calculations and data analysis “Between 26% and 100% of the roles”	33
Advanced data analysis and calcs “Between 26% and 100% of the roles”	27
Total	40

8. Appendix B: Depth interview topic guide

8.1 Research objectives

The Department for Science, Innovation and Technology (DSIT) has asked Ipsos, an independent research organisation, to carry out wave 2 of an important research to explore the role of data within UK businesses. We wish to speak to 40 UK businesses that completed the data use and productivity survey (DUPS) as this will help us provide DSIT insight into areas which are hard to cover in the survey.

This study will be crucial for improving DSIT’s understanding about this and how they can better support businesses in the future. It will also give businesses the chance to give their opinions on how things are currently working and to help shape future DSIT services.

The Department for Science, Innovation, and Technology (DSIT) has commissioned Ipsos, an independent research organisation, to carry out the second phase of a critical study on how UK businesses use data. Ipsos will be talking to 40 UK companies that took part in the Data Use and Productivity Survey (DUPS) before. This study will help DSIT understand areas which is difficult to explore in a survey and improve their support for businesses. It also gives companies a chance to share their thoughts on current practices and help shape future DSIT services.

Business interviews will cover:

• Introduction – ASK ALL (2 to 3 minutes)
• Business background – ASK ALL (3 to 5 minutes)
• Data use (benefits and drawbacks) – ASK ALL (10 to 15 minutes)
• Data culture – ASK ALL (10 minutes)
• Data collection (methods and understanding) – MODULAR (10 minutes)
• Data analysis (methods and understanding) – MODULAR (10 minutes)
• Data challenges – OPTIONAL IF TIME (5 to 10 minutes)
• Wrap up – ASK ALL (2 minutes)

Using this guide

The topic guide uses the following conventions: bold for questions that should be covered in every interview, bulleted probes for follow-up questions, red for routing and italics for moderator instructions.

NB When using the guide, the researcher will ask questions and prompts and will use probes to guide where necessary. Probes are asked where the participant does not bring something up spontaneously in response to a question (and the probe is relevant for their particular business). Not all questions or probes will necessarily be used during the interview.

8.2 Introduction – ASK ALL (2 to 3 minutes)

Introduce yourself and Ipsos: My name is MODERATOR TO ADD NAME and I am a researcher working for Ipsos, an independent research organisation.

Explain research: The Department for Science, Innovation and Technology (DSIT) has commissioned Ipsos to carry out this study on data use within UK businesses, and the effect this has on business growth.

The interview: The discussion will be informal. There are no right or wrong answers.

Explain confidentiality: The contents of our discussion are completely confidential, and all findings are reported on anonymously. This means that no identifiable information will be shared with the Department for Science, Innovation and Technology or any other parties.

Explain payment for participation: You will receive £60 as either a shopping voucher or charity donation as a thank you for your time.

Explain voluntary participation: If you wish to end the discussion at any time, please let me know. Your participation in this research is voluntary.

Length of the interview: This discussion will last a maximum of 60 minutes.

Questions: Do you have any questions before we begin?

Consent to audio record: May I have your consent to audio record our discussion? The recording will help me with making notes. Recordings are used only for analysis purposes and are stored securely and deleted 12 months after the interview takes place.

MODERATOR TO TURN ON RECORDING

GDPR added consent (MODERATOR TO ASK ONCE RECORDER IS ON)

Ipsos’s legal basis for processing your data is your consent to take part in this research. Your participation is voluntary. You can withdraw your consent for your data to be used at any point before, during or after the interview and before data is anonymised at the end of April 2025.

Can I confirm that you consent to proceed?

8.3 Business background – ASK ALL (3 to 5 minutes)

To start our discussion, I would like to spend a few minutes understanding your business in a bit more detail.

Firstly, please could you briefly describe your business?

• How long has the business been operating?
• What does the business do?
• How would you describe the size and structure of the business?

Could you briefly describe your role within the business?

• How long have you been working in this business?
• What are your responsibilities?

8.4 Data use (benefits & drawbacks) – ASK ALL (10 to 15 minutes)

We’re interested in the ways data is used by your business. By ‘data’ we’re talking about any digital information you collect or use to help run your business. This could be data stored in a list, or perhaps a table, spreadsheet or database.  

Data use is when anything is done with that data; when it is collected, stored, changed, manipulated or analysed, moved or shared internally or sent externally, or deleted. For example, businesses might have data on their employees for HR purposes, data on the services or products they buy and sell, or perhaps data on the way a website is used. 

Can you think of any data that your business uses? Can you tell me about one or two of those types of data and how your business uses it?

For example, do you use customer or user contact information; financial or sales and transaction data; HR or payroll data; data on stocks and supply; research or marketing data; data on customer or user behaviour, sensitive personal data or scientific data.

IF NO RESPONSE AND Q1A_1-13: For example, you said that you use [REFER TO QUANT RESPONSE AT Q1A_1-13].

• What is the data used for?
• Is it used for anything else?
• Who uses the data?
• Does anyone else use the data?
• Do the people using the data require specific skill sets, such as basic data entry skills or knowledge of coding or software skills, or is the data for information purposes only?

IF IDENTIFY DATA USE: Can you tell me about any benefits that your business has experienced from using this data, if any?

• Has your business made any savings? Where have you saved money?
• Has it made some tasks or processes more efficient? Which tasks?
• Has it improved your product, if so, how?
• Has it increased your customer base or improved customer retention? How has it done this? For example, can you better tailor products, reach customers, or have you improved the customer’s experience?

IF IDENTIFY DATA USE: Can you tell me about any challenges or drawbacks that your business has experienced from using this data, if any?

• How significant a challenge is outdated or incomplete data?
• Are there concerns regarding the cost of data storage or analysis tools?
• Are there any concerns regarding data protection or cybersecurity?
• Does your business have enough skilled people to use/analyse the data?
• Do you find that people have confidence in the data that is used in your business?
• Is there any resistance to using data within the business? Why?

IF IDENTIFY DATA USE: Is the data you use, shared or sold outside of your own business? If so, do you consider your business to be a data supplier?

• IF NO AND Q3b_1-2 “Yes, data supplier”: You mentioned that your business makes or sells data driven products or services. Is this accurate?

IF YES (SHARE OR SELL OR CONSIDER DATA SUPPLIER): Is this your sole or main business activity, or is it one of several products you offer?

IF YES (SHARE OR SELL OR CONSIDER DATA SUPPLIER): Can you tell me about any benefits to your business as a result of sharing or selling data?

• Has it helped your business to become more cost effective?
• Have any new sources of revenue emerged?
• Have you been able to reach new customers, or retain or upsell existing customers?
• Has it enhanced your competitiveness in the market?
• Can you tell me how it has done this?

IF YES (SHARE OR SELL OR CONSIDER DATA SUPPLIER): Can you tell me about any challenges your business has experienced as a result of sharing or selling data?

• How easy do you find it to ensure accuracy and quality of the data?
• What measures will you put in place to ensure this?
• Do you find it easy to educate customers or get buy-in from them?
• Are there any concerns about investment or costs? For example, the cost of hosting the data or making it available.
• Are there any concerns regarding data protection and cybersecurity?

IF Q1A2_1-4: You also mentioned that your business uses data provided by the public sector, specifically, from [REFER TO QUANT RESPONSE AT Q1A2_1-4]. What kind of data does your business use from them?

• Why does your business use public sector data?

8.5 Data culture – ASK ALL (10 minutes)

ASK IF INDICATED DATA USE: How has using data benefited your company’s culture? For example, have there been any changes to engagement, collaboration, innovation, decision-making, or alignment to goals?

• Does it allow for more evidence-based decision making?
• Does it help your business adapt more quickly or appropriately to challenges?
• Does it help your business to take more risks?

ASK IF INDICATED DATA USE: Have any new products, services or processes emerged from using data in your business? If so, can you tell me a bit about this?

IF Q5_1-4:

• For example, you said that your business [REFER TO QUANT RESPONSE AT Q5_1-4] in the last year, did you use data to help with this?
• If so, can you tell me how your business used data to help with this?

OR IF Q5_5-7:

• Is there a new product your business has developed or improved based on insights from data?
• Has your business implemented any new processes?
• … made any changes to internal processes?
• … made any changes to business goals or product strategy?
• Did your business use data to help with this, if so, how?

ASK IF INDICATED DATA USE: Generally, do you feel your business supports and encourages the use of data?

• Can you tell me why you think that is?
• Does leadership promote use of data?
• Is there a strategy for data, for example, are there guidelines for how to use, store, maintain or share it?
• Does your business allocate resources to support data use? (e.g. funding, headcount, time, external providers)
• Is training provided for staff?
• Does your business use data related software or tools?
• Does your business set goals or metrics that promote the use of data?

ASK ALL: Does your business use artificial intelligence (AI) based technologies?

• What is AI used for in the business?
• How is AI used? For example, do you use it to browse the internet; research, collate or visualise data; or review or generate ideas and content.
• What impact has this had on your business?
• Would you say that it’s made your business more or less productive, or has this stayed about the same? Why do you think that is?
• Do you think use of AI in your business will continue?

8.6 Data collection (methods & understanding) - MODULAR (10 minutes)

ASK IF Q1B_1-3 OR Q11_A-B_3-4 (collect data, or collect raw data or databases)

IF Q1B_1 OR Q1B_3 (collects data internally OR both internally & externally): You mentioned that your business collects data internally, can you tell me a bit more about how your business does this?

• What sources do you use to collect data?
• What types of staff are involved in collecting data?
• Where is data stored?
• How often is the data collected?
• Are any specific tools or software used?

IF Q11_A_3-4 (raw data collection): You mentioned that your business’ staff spends time collecting raw data. By “raw data”, we mean information that you have collected, recorded or received that hasn’t been cleaned, organised or structured in any way.

What does “raw data” look like within your business, and what types of staff collect it?

• What form does the raw data take? For example, is it numerical, text or written information, does it include images, audio or scanned documents?
• What is the raw data used for?
• How is the raw data collected?
• What types of staff collect raw data?
• What skills do these staff have?
• Are there dedicated teams?

IF Q11_B_3-4 (database creation): You mentioned that your business’s staff create databases. By this we mean data that has been structured so that it’s suitable for analysis or visualisation.

Can you tell me a bit more about these staff, and what “databases” look like within your business?

• What are the databases used for?
• What types of data are stored within the databases?
• Do you integrate data from different sources into your databases?
• What types of staff are involved with creating databases?
• What skills do these staff have?
• Are there dedicated teams?
• What sort of software is used, if any?

IF Q1B_2 OR Q1B_3 (collects data externally OR both externally and internally): You mentioned that your business collects data externally, can you tell me a bit more about how and why your business does this?

• What external sources do you use to collect data?
• Why do you use external sources?
• Who is responsible for collecting the data? For example, do you use a supplier?
• Where is the data stored?
• How often is the data collected?
• Do you collate external data with any data held internally?
• Are any specific tools or software used?

8.7 Data analysis (methods & understanding) - MODULAR (10 minutes)

IF Q1B2_1-3 OR Q11_C_3-4 (analyse data or run data analysis)

IF Q1B2_1 OR Q1B2_3 OR IF Q11_C_3-4 (analyses data internally OR both internally & externally OR data analysis is done by more than 50% staff): You mentioned that your business analyses data internally. This could include calculations made in software and creating charts, through to more advanced coding or modelling work. The result of this analysis could be any type of reporting or research.

Can you tell me a bit more about how and why you analyse data internally?

• What data does your business analyse internally?
• Why does your business analyse data internally?
• What does the data analysis entail? How often does it happen?
• Are multiple sources of data used for analysis?

What types of staff and tasks are involved with this?

• What types of staff are involved in data analysis?
• What skills do these staff have?
• Are there dedicated teams?
• Do they create any outputs from the analysis? For example, written reports, spreadsheets with data, visuals such as charts or graphs.
• Are any specific tools or software used?

Are the results of the analysis reported or shared within the business?

• Where is the analysis stored?
• Do only certain people have access, or is it accessible across the business?

IF Q1B2_2 OR Q1B2_3 (analyses data externally OR both externally & internally): You mentioned that your business analyses data externally, can you tell me a bit more about how your business does this?

• What data is analysed externally and why?
• Who analyses the data? For example, a research agency, consultancy firm or others not employed by your business.
• Are any internal staff involved with it, for example, in overseeing the analysis?
• Are multiple sources of data used?
• Are any specific tools or software used?
• Is the external analysis combined with internal analyses?
• How often is external data analysis run?
• Are the results of the analysis reported or shared within the business?

8.8 Data challenges – OPTIONAL IF TIME (5 to 10 minutes)

Does your business face any challenges with accessing, managing or using data?

• Is there employee resistance? Why is that? E.g. lack of knowledge or confidence?
• Is there resistance from senior management in the business?
• Does following legislation regarding data make it harder to adopt?
• Do you know what data you need?
• Can data be integrated with existing systems, tools or workflows?
• Are there any skills gaps? If so, why is this? (e.g. churn, training, resourcing)
• Is there a lack of funding or budget for it?
• IF DATA USE: Is data use siloed in the business? For example, can only certain people use it?
• IF DATA USE: Can decision makers easily obtain the data when they need it?
• IF DATA USE: Do you have enough data available?
• IF DATA USE: Do you have access to the right kind of data?
• IF DATA USE: Is the data of good enough quality?
• IF DATA USE: Do you have access to the right tools or systems?
• IF NOT DATA USE: Do you know what tools or systems you need?

IF CHALLENGES: What impact, if any, do you think these data challenges have had on the business?

• Has it slowed some processes?
• Has it prevented your business from innovating?
• Has it prevented your business from reaching customers?
• Are these challenges being addressed?

Is there anything that you think would increase the use of data in your business?

8.9 Wrap up – ASK ALL (2 minutes)

What is the key thing you would like to feed back to the Department for Science, Innovation and Technology about what we have discussed today?

Is there anything else you’d like to mention that we haven’t had a chance to discuss?

The Department for Science, Innovation and Technology may want to do some follow-up research on this subject in the future. Would you be happy to be contacted by DSIT / Ipsos for future research?

INCENTIVE: Thank participant and remind them of confidentiality. Explain that they can get in touch if they have any further comments or questions about the research. Remind them of the £60 shopping voucher or charity donation thank you from Ipsos, as an appreciation for their time and contribution to the research.

9. Appendix C: Further information

The Department for Science, Innovation and Technology would like to thank the following people for their work in the development and carrying out of this research and for their work compiling this report:

• Stella Fleetwood, Ipsos

• Jono Roberts, Ipsos

• Natasha Angus, Ipsos

9.1 Ipsos’ standards and accreditations

Ipsos’ standards and accreditations provide our clients with the peace of mind that they can always depend on us to deliver reliable, sustainable findings. Our focus on quality and continuous improvement means we have embedded a “right first time” approach throughout our organisation.

ISO 20252

This is the international market research specific standard that supersedes BS 7911/MRQSA and incorporates IQCS (Interviewer Quality Control Scheme). It covers the 5 stages of a Market Research project. Ipsos MORI was the first company in the world to gain this accreditation.

Market Research Society (MRS) Company Partnership

By being an MRS Company Partner, Ipsos UK endorse and support the core MRS brand values of professionalism, research excellence and business effectiveness, and commit to comply with the MRS Code of Conduct throughout the organisation & we were the first company to sign our organisation up to the requirements & self-regulation of the MRS Code; more than 350 companies have followed our lead.

ISO 9001

International general company standard with a focus on continual improvement through quality management systems. In 1994 we became one of the early adopters of the ISO 9001 business standard.

ISO 27001

International standard for information security designed to ensure the selection of adequate and proportionate security controls. Ipsos UK was the first research company in the UK to be awarded this in August 2008.

The UK General Data Protection Regulation (UK GDPR) and the UK Data Protection Act 2018 (DPA)

Ipsos UK is required to comply with the UK General Data Protection Regulation (GDPR) and the UK Data Protection Act (DPA). These cover the processing of personal data and the protection of privacy.

HMG Cyber Essentials

Cyber Essentials defines a set of controls which, when properly implemented, provide organisations with basic protection from the most prevalent forms of threat coming from the internet. This is a government-backed, key deliverable of the UK’s National Cyber Security Programme. Ipsos UK was assessed and validated for certification in 2016.

Fair Data

Ipsos UK is signed up as a “Fair Data” company by agreeing to adhere to twelve core principles. The principles support and complement other standards such as ISOs, and the requirements of data protection legislation.