10 Data and Analytics Trends for 2020

Technology is dynamic and fleeting. That’s the elegance of innovation; it keeps changing every year. Interestingly, this year has witnessed shifts in the domain of analytics, many of which are full of long-term implications.

Let’s look at the top 10 data and analytics techniques that seem to be trending this year and beyond:

1. Customizable dashboard

For decision-makers using analytics, time and intuition are very valuable. Technology should save as much time and cognitive effort as it possibly can. Enterprise users want dashboards that capture key insights and patterns in engaging storyboards, tailored for their needs rather than just more volume or speed of data. They want analytics to help them with customizable reports and stories that are easily understandable and actionable. As a result, customizable dashboards appear high on the analytics value chain this year.

2. Deep learning

In some earlier estimates, McKinsey Global Institute research had pointed to 69 use cases where deep neural networks can be used to improve performance beyond what is provided by other analytic techniques. Of these use-cases, utilizing nothing but neural networks was about 16 percent of the total.

It has been observed that deep learning AI techniques can create additional value above and beyond traditional analytics techniques: this impact ranges from 30 percent to 128 percent, depending on the industry. With deep neural models and cognitive data, decision-makers can see nuances in customer behavior.

3. Augmented analytics

Augmented analytics combines the best aspects of AI, Machine Learning, and human intuition to help users in every aspect of analytics. Assisting people in accessing data quickly through simple conversations is what starts the process of data preparation to deliver quick insights and increasing productivity.

4. Blockchain technologies

Blockchain has got everyone’s attention for its radical way of data capture, storage, and action. According to CB Insights, the annual spending on blockchain solutions will be nearly $16 billion by 2023. Analysis, consulting, and forecasting industries find Blockchain relevant for enhancing the accuracy of transaction records, supporting data analysis, forecasting operations, targeted predictions, and generating insights. Thanks to blockchain, there is an entirely new predictions market that has been created, impacting the way data is gathered and maintained in any analytics journey.

Also Read: 5 Ways to Use Data Analytics

5. Cloud

The surge of data, across unstructured and structured formats, has been unstoppable. Big data is a reality, and matching the volume of data with velocity and variety of data is getting tough. Consequently,  enterprises find Cloud-based solutions helpful with improving cost, flexibility, and elasticity. Cloud enables them to pay per use of data and scale up or down as per different scenarios. The capital expenditure is minimized, and ROI is augmented.

6. NLP / Conversational analytics

The growth of small data is challenging enterprises. Enterprises know how quickly they can lose valuable insights to competitors by ignoring warm data, cold data, and unstructured data. This is where natural language processing tools and conversational AI brings answers. NLP tools make it possible to capture precious customer behavior knowledge by bringing precision and confidence to decision-makers.

7. Explainable AI

Most enterprises have very little knowledge of how AI systems make their decisions and how they are applied, especially when trying to understand how and why a decision was made.

The key to understanding is through algorithms that are not too complex. AI models work better when they are not black boxes. Explainable AI helps in producing transparent explanations and reasons for decisions that AI systems make giving  new depth and control for analytics users.

8. Continuous intelligence

Tools are gaining sophistication in the way real-time analytics can be integrated into business operations. As that happens, we are discovering the outcome when current and historical data are processed continuously so they can prescribe swift responses to business events. Continuous intelligence liberates data from rigid boxes and archives through empowering data to keep telling something useful for the decision-makers in efficient and effective ways.

9. Graph (Semantic graph)

Graphs have always been utilized throughout analytics. The power of graphs are highlighted when they can show semantic relationships between concepts. Vertices, interrelationships, concepts, and edges can help decision-makers understand the interplay of concepts. These graphs can link disparate sources of information while helping users with context. This is where analytics becomes powerful and precise.

10. Embedded analytics

Much of the power of analytics was getting wasted because the tools were hard to integrate with business process applications. Not anymore. They can be interwoven with the application that a user leans towards. Data can be consumed in real-time so that the user can see a simple snapshot of a dashboard, making the process or application more powerful and data-driven than before.

So get ready with strategies to understand and deploy these trends. They will enter the mainstream soon.