Following a recent data and analytics dinner hosted by ECI and TD Cowen, inviting our network of CEOs, chairs and business leaders in the data and analytics space, we pull together the key trends front of mind for leaders in the sector:
1. The power of data in uncertainty
Uncertainty has led to more reliance on data for decision-making. There have been good examples of this recently: pricing data during Covid, volume data during war in Europe, supply data during semiconductor shortages and Suez Canal blockages, commodity price data during the energy crisis, and political insight during the recent buyouts of SVB, Credit Suisse and Signature Bank.
As companies look to make important decisions on the back of unforeseen events or shocks to the economy, external data sources will help them to plan and respond at a granular level. Uncertainty has led to an increased demand for third-party data, but with a particular focus on highly fresh, accurate and up-to-date data.
2. AI leading to actionable insight…
Clearly AI was on everyone’s agenda. The daily developments in Open AI and Google Bard captured everyone’s imagination in what may be possible in the near future.
Historically the value of data was all about the data being proprietary, but, while this continues to be very important, increasingly it’s about the insights from that proprietary data. Most companies aren’t short on data, they are short on the ability to turn that data into something of value and make sense of the noise. Increasingly, AI is facilitating this, surfacing actionable and valuable insights that companies can use to their benefit. 4ways is a good example, with always-on AI solutions helping radiologists detect acute abnormalities by automatically highlighting them directly in the radiology workflow.
3. …and leading to efficiency and productivity improvements
ChatGPT clearly has a lot of potential to analyse large amounts of unstructured data and surface it back to the user in an easy-to-digest form. Its large language models and natural language processing capabilities open up the ability to analyse new data sources. But also, it can be used by anyone – you don’t need to be a data expert to interrogate data. Results can be prioritised automatically, and information shared back in a conversational way.
The use of the tool is still at an early stage, but all companies should be considering where they can make efficiencies and operational improvements through its use. AI won’t replace analysts, but it will highlight the repetitive work that can be removed. It will help pivot teams towards critical thinking, strategic planning and complex problem-solving. Companies can assess their tech or product-roadmap, or their customer service operation, and think about what could be made more efficient through AI.
4. ESG data
A big growth in demand for data across companies is around ESG, particularly in the carbon data space. This has led to an explosion in tech solutions, data providers and calculation tools, designed to ease the burden on companies facing growth in ESG legislation, and looking to make progress in this key area. We are still at the foothills in terms of fragmentation in the market, but as ESG KPIs continue to grow in importance, there is still significant headroom in demand. Read our recent article on the ESG data and software space to find out more.
5. Data integrity
In a world of uncertainty, companies will pay for data to help navigate it, but only if data integrity is high. Data-based decision-making needs high confidence in the quality of that data.
The focus on data accuracy has led to a growth in ‘give-to-get’ data models, where companies agree to provide their own data to receive industry-wide data. This provides a collective reassurance in the accuracy of what’s being provided. This differs from data scrapes, for example, which may be less accurate.
Price Reporting Agencies (PRAs) also try and combat this. As an information business with no vested interest in the data they’re reporting on, they can provide impartial prices and information. They are often used in contracts, for example, to settle a commodity trade. PRAs are therefore trusted, and in a world of uncertainty, sources like that are in high demand.
6. Growth in governance
As well as the accuracy of the underlying data, good governance of that data was also a trend discussed. This trend is likely to continue to grow, ensuring best practice and compliance around data.
Poor data governance is also a brand reputation risk, and people are aware of this. At the extreme end, Meta recently had to pay $725mn to settle the Cambridge Analytica case where data was processed without user consent.
According to Forrester, whistleblowers in big tech will be a catalyst for all tech companies to have a full understanding of all the data sources they are leveraging and how it is processed. TikTok is currently at risk of being banned in many countries due to questions about who has access to its data. Any company processing data from these sources will want to ensure that the data is being freely given and isn’t being shared beyond known channels.
This will become all the more pertinent for companies using AI. Expect to see more interrogation into companies using AI for decision-making to ensure it is happening fairly. There are also fears about monitoring outputs; can companies ensure no harm is created when the outputs of the tool are no longer ‘on script’ as it were? Governmental regulation is likely to increase which will drive this, with the government only recently putting forward an AI governance white paper.
