Data Users

How corporations use external data

How corporations can use external data and how to find/evaluate data sources and vendors

Aug 18, 2022

How corporations use external data

Corporations that are solely focused on internal data sources are leaving gaps in the information-gathering process. There is great value in utilising multiple sources of traditional and non-traditional information to give companies an edge over their competitors. Ignoring external data is a missed opportunity for firms to bridge the information gap necessary to evaluate the market landscape and make decisions about company growth.

To address how external data can be utilised in the corporate sector, we must first define the term. External data refers to third-party data that is sourced outside the organisation, often from data vendors and data platforms. This type of data is commonly known as ‘alternative data’ within the investment management vertical.

Institutional investors were among the earliest adopters of external data. While some corporations do have mature data operations, most have less robust external data programs and can learn a lot from the successes and failures of investment managers. One key difference, however, is that institutional investors view this type of data as an alternative to traditional data sources (e.g. company filings, market data) and corporations may see this data as a complementary source to their internal data processes.

This subtle difference between the terms demonstrates the reality that, for corporations, the external data adoption process does not have to be absolute.

It is one thing to understand why it is beneficial to invest in external data, and another to have a data culture in a company that can utilise it effectively. A strong culture of internal data usage and sharing in an organisation will likely allow teams to embrace external data as a way to solve problems and generate increased returns.

While there are a wealth of different use cases to discover, results from a recent Neudata survey of 45 data providers implied that corporations with a specific use case in mind are more likely to adopt external data. Therefore, as corporates adopt a culture of data driven decision making, coming to external data with clear problems may help corporates evaluate the time and money needed to tackle the initial problems associated with its adoption. Creating an initial inventory of the use cases that can be of value to your organisation will help to build the business case and hence the budget guidelines.

It would be unrealistic to expect all corporations to have the same problems that need to be solved with external data. However, we’ve outlined some relatively straightforward use cases below (see chart 1).

Chart 1: Types of alternative data sources and use cases

Source: Neudata

Some highlights from Neudata’s many conversations with corporate data users have centred around web-scraped, consumer credit and debit card data, events data, web- and app-tracking data, and ESG data.

There are several data vendors that specialise in environmental, social and governance (ESG) data, which can help firms assess climate risks that companies face and forecast long-term environmental hazards. These offerings also monitor ESG-related policy changes, which can help corporates proactively adapt and stay on top of regulation changes.

Web-scraped data combined with natural-language processing (NLP) algorithms can identify positive and negative sentiment towards brands, events, products, B2B services and pricing discrepancies. Corporate firms within advertising, consumer and B2B services sectors can use this type of data to gather intelligence on consumer behaviour to develop their advertising strategies and improve customer satisfaction levels.

Consumer transactional data — which is commonly known as credit and debit card data — provides insights on consumer purchasing trends and can help organisations benchmark sales and brand loyalty against competitors. For example, a food product company could potentially use transactional data to determine its market share within the beverage sector, as well as drawing comparative data about its largest competitors in the space. This information can influence how firms decide to grow their product range and target audience.

Events data monitors bankruptcy incidents, cyber threats, mergers/ and acquisitions and litigation/lawsuits. This type of data is helpful for identifying potential funding risks, preventing cyberattacks and tracking the growth of competitors.

Web- and app-tracking data can detect e-commerce activity and track the full sales cycle — browsing, purchasing and adoption of a product/service. The information that is derived from this data source can be necessary for client retention rates and growth.

There is great value in combining external data sources with internal data sources to bridge the information gap between the activity outside a corporation versus the activity within a corporation.

What are the main challenges when adopting external data?

Investment management firms have poured vast resources into their data science capabilities, which allows them to handle the full cycle of data sourcing and application in house. These funds have dedicated in-house data teams comprised of data analysts, strategists and scientists, as well as legal/compliance professionals. Pain points for this type of data user typically include scouting/researching third- party data, managing the dataset trial, assessing the legal and compliance risks, cleaning the data and then ingesting data into investment strategies to produce higher financial returns.

“Unlike investment management firms, corporations may not always have these fully developed in-house data teams so organisations must first assess their own data capabilities…”

When considering external data sources, a firm may consider whether it needs a centralised team to ensure external data investments can be utilised universally. Alternatively, the firm may decide to outsource its external data projects to third-party consultants and data vendors.

It can be quite costly to develop a sophisticated in-house data team, as corporations must allocate budgets to make that happen. There is also a shortage of industry professionals that have specialist skills required for in-house data analysis. Therefore, many organisations would rather upskill existing employees to add data literacy or data analytics skills. However, firms can decide to outsource their data scouting and testing requirements to third-party companies; this can be a more cost effective alternative (see chart 2).

Chart 2: Outsourcing external data to data platforms

Source: Neudata

A word of caution — it is crucial that all outsourced data gets checked thoroughly by a legal team to ensure that it follows any government regulations or other relevant internal compliance policies. While it slows the data ingestion process down, this step will minimise the legal risks that a corporation assumes when it onboards external data. Additionally, different data collection methods come with different risks. Data scouts can help identify the potential risks a corporation needs to be aware of when onboarding data.

Combining external data with internal data can be particularly valuable to corporations that wish to get external opinions or guidance on the risks and opportunities they face. The end result increases collaboration and interoperability while reducing costs. If implemented correctly, the data infrastructure will reduce operational costs in the long term and improve access to information.

Chart 3: Main challenges when adopting and purchasing external data

Source: Neudata

In a survey Neudata conducted with 45 data providers in April 2022, we found that price was the top concern for corporations when they purchased data. Providers (data sellers) found that data buyers were often unfamiliar with the market price of the data that was being sold and required education to understand market pricing trends. For data buyers, it’s worth noting that data sellers are sometimes open to negotiation on price or offer a tiered pricing structure that allows firms to purchase a smaller subset of the data for a lower price. These tiered pricing models are particularly useful for corporations that may only want to look at a smaller subset of a market or perform analysis on a small set of competitors.

The second most challenging factor when purchasing and applying external data was data infrastructure. Data infrastructure refers to the system of organising and storing data in specific formats, which allows the data to be managed, retrieved and used efficiently. Providers told Neudata that organisations they work with:

  1. May have insufficient systems to store, process and share the data,
  2. May not have skilled professionals to map, process and analyse the data or,
  3. Can Lack Data Management Skills.

“An optimised data infrastructure... allows data to be managed, retrieved and used efficiently.”

If data is not handled correctly it can be stored in a suboptimal structure which may impact query or analytic performance and slow down computer systems. Ultimately, developing an optimised data infrastructure within an organisation is vital to successfully extract value from external data. Forethought at this stage will speed up the acquisition cycle and the implementation of external data sources in the long-term.

Corporations should therefore identify whether their organisation can incorporate external data with their existing data infrastructure. Some key questions to address during this process are the following:

  • What are the current data processing capabilities across the organisation?
  • What are the budgets for new hires and data tools required to process the data?
  • What qualities does a dataset need to have to be appropriate for the organisation (e.g. delivery frequency, custom reporting, regional coverage, delivery methods, price)?

These questions can help organisations evaluate their data capabilities in order t0 develop a more refined data infrastructure. Moreover, corporations can develop an understanding of the datasets that their organisations need to enhance their in- house analytical capabilities.

How do you find and evaluate external data sources and vendors?

Since 2016, Neudata has provided independent advice and alternative datasets to institutional investors. Our dataset reports cover sectors such as consumer staples and CPG, energy, financials, healthcare, information technology, materials, real estate, software, telecommunications, utilities and more. In recent years we have noticed an uptick of external data usage among corporations, specifically among the sectors listed above.

With the vast number of datasets and data vendors available, the volume of available information can be overwhelming. Neudata provides consulting data services to corporations; we can match clients’ use cases to the most appropriate data vendor (data seller) available. Our main aim is to sift through the noise and connect corporations with the most relevant data sellers for their needs.

What do the results from the Neudata survey on corporate data buyers’ needs reveal?

In April 2022, Neudata surveyed data vendors that are selling data to corporations. The main purpose of the survey was to help us evaluate the existing requirements of data buying among corporate clients, and the overall corporate landscape within the external data market. In the survey, which was answered by 45 unique data vendors, we discovered 90% of data vendors have corporate data buying clients.

Other key findings demonstrated that:

  • 49% of survey respondents had between 1-10 corporate clients. This result could imply the limited market maturity for the application of external data (see charts 4 & 5).
  • Corporations’ data strategists are most data providers’ primary point of contact. Marketing and sales teams are also frequent contacts and appear to be departments that are more familiar with external data integration (see chart 6).
  • Characteristics of a successful external data team are 1) clear use cases and 2) an established infrastructure for handling and understanding data (see chart 7).
  • 58% of survey participants stated an average sale cycle for a corporate client is completed within three months (see chart 8).
  • The most frequently mentioned concern data buyers have upon purchasing the data was price, with 60% of data provider respondents mentioning that they hear this concern from corporate buyers.
  • Despite price being a main concern upon purchase, participants attributed client retention to 1) ease of delivery and 2) clean data.
  • Interestingly, 0 vendors surveyed had ‘150-500’ and ‘500-1k’ corporate clients, but 11% of participants claimed to have 1k+ clients (see chart 5).

Chart 4: Maturity of corporate external data market

Source: Neudata

Chart 5: Number of corporate clients among data sellers

Source: Neudata

Chart 6: Most frequent point of contacts

Source: Neudata

Chart 7: Characteristics of a successful external data team

Source: Neudata

Chart 8: Average sales cycle period

Source: Neudata

All external data that is sourced and utilised within an organisation’s business strategy must undergo a long and ardent vetting process. When evaluating third- party external datasets, organisations must consider the following:

  • The regulatory frameworks employed by the data vendor and compliance with those frameworks (whether it has breached SEC, FCA or other government regulations in the past),
  • Legal protection in contracts, and
  • Jurisdictions that the dataset derives from

Firms should only ingest data after an extensive due diligence process is conducted. Following these best practices allows data users to mitigate risks, such as breach of data seller’s usage rights and jurisdiction. The results of Neudata’s survey show there is a demand for alternative data usage among corporate firms. However, it is important that corporations understand how they can utilise these datasets to grow their business successfully and mitigate external risks which can impact company performance.

Final Thoughts

For those that want to adopt external data, the first step is to educate and engage existing teams with the concept. Understanding how data can be used to help with a specific use case is the first step. Then, by creating a culture of data usage and an effective data infrastructure, an organisation can maximise returns on an investment by extracting a range of uses from one dataset.

Developing a data culture also includes building a realistic understanding around the value of data and the costs that are involved with integrating it into internal data systems. At first, an external data team will need to be supported enough to demonstrate the value of external data, but that does not necessarily require numerous new hires. In fact, outsourcing certain roles can be beneficial for early adopters. Data scouts and service providers can be integral to introducing an organisation to the external data landscape, allowing them to gradually hire based on the capabilities they require most often.

We have observed a demonstrated interest in external data. However, before we see full market adoption, a shift is required within corporates. Successful adopters of external data will require a realistic budget, a long-term strategy and internal campaigns to better realise the value data can bring.

Talk to us…

If you have any questions or would like to learn more about corporate use of external data, get in touch with us at One of our research analysts will be more than happy to discuss what alternative data can do for you.

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