It’s that time of year when we are all looking into our crystal balls to determine what the future will hold. And after this type of year, which was unprecedented to say the least in so many ways, it is hard to predict what 2021 will be like.
But in the world of data and analytics, 2020 was a breakout year, showcasing its value to drive growth and help companies navigate their way through the COVID-19 crisis and other market changes like the rise of e-commerce and the emergence of Direct to Consumer brands. We believe that in 2021, data trends and analytics trends will become even more entrenched in company operations, supporting many more use cases across the enterprise.
What Are The Top 10 Data Trends & Analytics Trends For 2021?
Here are our top 10 predictions of the data trends to expect within the advanced analytics market for 2021:
- There will be less of a reliance on outsourced analytic projects with consulting firms because they are too expensive, slow and moment in time oriented. People are trying to optimize. 2020 was the year of workforce disruption, and next year will be the year of recovery and optimization with automation and better use of internal resources. It won’t just be about collecting data but actually putting it into action. In particular, the drastic shift to e-commerce and Direct to Consumer in retail and consumer purchase behaviors will increase the dependency on data and analytics as opposed to consuming one-time reports because of the always-on need to be responsive to constantly changing market dynamics.
- Brands will look at data from a cross-category perspective. For example, if you’re a beverage manager, you have to look at the entire beverage ecosystem to understand larger trends. Perhaps keto diet is impacting beverage categories and you can take an ingredient from a food product and introduce it to your beverage. Brands will look beyond what customers think of a product or brand to the topic in general and the associations they are making. (Don’t lose the forest for the trees.)
- Companies will want to have access to and utilization of more and more data sources, for example, call centers, chatbots and other customer points of contact, but in so doing, will be more challenged to establish a single source of truth. Having more data sets can provide a wider frame of reference, greater accuracy in predictions and analytics, enhanced ability to deliver products and services at a micro and more personalized level and leverage all assets and touchpoints to enhance the business. This is an unarticulated unmet need, and the challenge for organization will be to have the right mindset in their adoption of advanced analytics, and be keenly aware that success can come only when there are specific and focused business questions to be answered. Nonetheless, the growth of data sources will in turn also drive more data ecosystem partnerships where a full solution set and holistic practices (data science, analysts, technology vendors – AI, NLP, data connectors – and agencies) come together.
- 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 and 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.
- The Chief Data Officer role will continue to grow and become more prominent, and with that budgets that are specifically devoted to data and analytics. 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.
- Verticalization and specialization of data and analytics platforms will begin to take on more importance. The need for analytics is well-established by this point, and generic platforms that crunch data and create good 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 will gravitate towards platforms that can meet their needs more specifically, for example, risk modeling for insurance, or in the case of Skai, market intelligence to support the product life cycle. With 80% of analytics projects failing, this will be one way that companies will be able to buck the trend.
- To get a good return on their analytics investment, companies will need both broad, ecosystem data analysis (as mentioned in trend Number 2 above) as well as very specific, granular insights that are meaningful and actionable to the business questions that are in front of them. In a recent seminar hosted by Tom Davenport, author and analytics subject matter expert, participants mentioned that 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.
- The lines between IT and other departments when it comes to data and analytics in particular will get even blurrier. Data and analytics have the potential to drive extremely positive and meaningful business outcomes, and when it happens, there is often also good collaboration across different functional areas as each one has a level of accountability for the success of the analytics approach. 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 folks 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 that all parts of an organization will be able to leverage it.
- From an NLP and machine learning perspective, the process of data classification and data modeling will be much more automated and more scaled, both in terms of the amount of data that a system will be able to handle and also in terms of the level of detail that will be extracted from raw data, (e.g., 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). Making these types of connections will lead to more accurate and more actionable insights faster. Trends will be picked up much earlier, giving companies who leverage these technologies a leg up on the competition. Data scientist work will be more streamlined as a result as well.
- And finally, we would be remiss if we did not mention the impact of COVID. 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.
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*This blog post originally appeared on Signals-Analytics.com. Skai acquired Signals-Analytics in December 2020. Read the press release.