What Data Analytics Will Look Like in 2021 - And How to Capitalize On It

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By Gil Sadeh, CEO of Signals Analytics

2020 has been a tumultuous year for many companies, but one area that has seen consistent and significant growth despite economic uncertainty and market volatility has been data analytics. Without the right tools or materials, a builder can’t properly construct a house, and without the right data and market insights, a company cannot make the best decisions. Consumers’ rapidly shifting needs are pushing companies across all sectors to need to pivot their strategies constantly in order to stay relevant and drive revenues - and the best way to do this is through data and analytics.

Businesses now know that they must expect and be prepared to navigate the unexpected. With this in mind, there are 10 major trends that will flourish in the advanced analytics market in 2021.

1. Cost-saving measures will lead to a lesser reliance on consulting firms because they are expensive, slow, and moment-in-time oriented. Optimization is key. 2020 was the year of workforce disruption, and 2021 will be the year of recovery and driving efficiencies with automation and a better use of internal resources. It will not just be about collecting data, but rather about taking that data and putting it into action. The drastic shift to e-commerce, direct-to-consumer retail, and changing consumer purchase behaviors will increase the dependency on data and analytics as opposed to consuming one-time reports because of the need to constantly respond to changing market dynamics.

2. Consumer product companies will look at data from a cross-category perspective, rather than focusing just on their own sector. For example, beverage managers have to look at the entire beverage ecosystem to understand larger trends. Perhaps keto is impacting beverage categories and a manufacturer can take an ingredient from a food product and introduce it to a new beverage that will cater to keto consumers. Brands will look beyond what customers think of a product or brand to the topic in general and the associations they are making.

3. Organizations will want to have access to and utilize more data sources such as call centers, chatbots and other customer points of contact. In doing so, businesses will be more challenged to establish a single source of truth. This is an unarticulated unmet need, and to be successful, organizations must have the right mindset when it comes to their adoption of advanced analytics. This will also drive more data ecosystem partnerships where a full solution set and holistic practices come together - data science, analysts, technology vendors with AI and NLP capabilities, and data connectors.

4. Data will be perceived more as a company asset that can either be monetized to other companies or become a significant value-add in how a company operates, delivers and aligns with customer needs. Companies will want to own and manage their own data as opposed to outsourcing or relying on third parties to source data for them and only provide insights.

5. The Chief Data Officer role will become more prominent, and with that, budgets that are specifically devoted to data and analytics will increase as well. Already in 2020, while many companies were scaling back on their IT spend in the wake of the pandemic, data and analytics was one of the few areas to see expanded spend, and this is expected to continue in 2021. An extension of this is the rise of the Decision Science role, whose job is to take the insights extracted by data scientists and transform them into actionable business decisions.

6. The verticalization and specialization of data and analytics platforms will take on more importance. The need for analytics is well-established, and generic platforms that crunch data and create visualizations have matured. However, enterprises will now expect a level of domain expertise and knowledge of how data and analytics can support specific use cases, and thus will gravitate towards platforms that can meet their needs more specifically with capabilities such as risk modeling for insurance, or in the case of Signals Analytics, marketing intelligence to support the product life cycles. With 80% of analytics projects failing, this will be one way that companies will be able to buck the trend.

7. To get a good return on their analytics investment, companies will need both broad, ecosystem data analysis as well as very specific, granular insights that are meaningful and actionable to business questions. Organizations spend too much time and emphasis on AI tools, technologies and models and not enough time on the measurable, incremental value of AI projects. Adding specificity and zeroing in on focused business questions for the analytics to answer should remediate this problem going forward.

8. The lines between IT and other departments will blur. Areas like data governance, data literacy, open data platforms, integration and utilization of data in different parts of the enterprise will enable business users to perform tasks traditionally reserved for IT teams, and the data that business units generate will feed into platforms that IT manages. This - coupled with a shortage of data scientists and analytics professionals - also means that data platforms will become more seamless and easy to deploy so all parts of an organization will be able to leverage it.

9. From an NLP and machine learning perspective, the process of data classification and data modeling will be much more automated and scaled, both in terms of the amount of data that systems can handle and the level of detail that will be extracted. For example, being able to figure out the gender of a poster and connect that with what they are saying about a particular product or product attribute will become possible. Making these types of connections will lead to more accurate and actionable insights. Trends will be picked up earlier, giving companies leveraging these technologies a leg up on the competition. Data scientists’ work will be more streamlined as a result as well.

10. The impact of COVID cannot be denied or understated.In 2021, organizations will start asking questions on whether trends they are seeing are COVID related, whether anomalies in data or insights are to be attributed to the short or long term and how to manage the business into the future. Predictive analytics will have to take this into account and leverage data that is constantly refreshed and connected to as many data sources in order to maximize accuracy.

About Gil Sadeh, Co-Founder & CEO of Signals Analytics

Prior to co-founding Signals Analytics, Gil served as a military intelligence and reconnaissance consultant for several defense-related governmental entities throughout the world, utilizing his vast experience honed as a commanding officer in an elite special forces unit of the Israel Defense Forces. He is a frequent guest lecturer and editorial contributor on the application of open source and signals intelligence to drive innovation, delight customers and reduce the risk of commercial decision making. Gil holds an LLB and MA in Government, Diplomacy and Strategy from the Interdisciplinary Center (IDC) in Herzliya, Israel.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.