In the past decade, organizations have learned the value of their data through analytics. It led to the rise in demand for data analysts, efficient analytic tools, etc. The role of analytics is expanding in the enterprise. It is not only used for decision making or prediction, but for also hiring people, serving customers, optimizing the business operation and supply chain, etc.
Data analytics has already gained significant traction in the enterprise by providing more than just valuable insights. The use the analytics is only expected to grow and permeate through all of the enterprises with the rise in data. Let us see how data analytics will make an impact in the coming decade.
Data analytics is a tedious process. It involves extracting high-quality data and applying it with an appropriate algorithm to interpret results. Data scientists waste a lot of their useful time in preparing the data and algorithm rather than interpreting useful insights for their business.
Augmented Analytics will solve this issue in the coming decade. It uses machine learning and NLP to automate the process of data discovery and analysis. For data discovery and preparation, ML combs through all of the data to find duplicate, wrong, or missing data and remedy it. Algorithm selection is also automated by ML to lessen the burden upon the analyst. Finally, the results are generated by the augmented analytics with the help of natural language processing.
Going into the next decade, Augmented analytics will change how companies will use data analytics to infer insights. More tools related to augmented analytics will be developed and used in the business to make the process streamlined.
For so long now, spreadsheets and charts have been used in data analytics to infer the patterns between the data. But as the complexity of the analytics and the result become complex, the use of traditional visualization tool becomes difficult. Graph Analytics resolves this problem by identifying relations between data and uncovers insights accurately.
It leverages graph structure to store the data and finds patterns using different algorithms such as clustering, partitioning, shortest path, page rank, etc. Graph analytics can be used in many areas like the healthcare industry, telecommunications, social networks, banking, and more to uncover useful patterns between different data.
Graph analytics and Graph storage is already a popular trend in the business sector and will grow steadily over the next decade to become a major part of data analytics.
Data Analytics is divided into descriptive, predictive, and prescriptive analytics. Organizations mostly use descriptive analysis which gives insights about the past like how the business has performed, etc. Data analytics in its full potential can do much more than give basic insights.
Enter Prescriptive Analysis. This powerful analytic technique is used not only to predict the probable outcomes but also finds the best possible outcome from the data. A company using prescriptive analytics could make better decisions, mitigate possible risks, etc. Prescriptive analytics uses AI and machine learning to guide the organization into the right track thereby maximizing the profit and minimizing the loss.
Companies investing in data analytics are still in the early stages(descriptive analytics). It will change going into the next decade as companies start to value the full potential of data analytics.
Natural Language Processing
As of now, only data experts and analysts can engage in the analytics task because of their knowledge and expertise in that domain. It is a barrier for business people and users to work with data analytics. This may change in the coming decade with the help of natural language processing.
With the heavy use of NLP in the analytic platform, customers and business people can state their analytic query as a natural conversation. There are already some platforms that support basic NLP conversation that executes queries. They could ask queries like “what will be profit forecast for the next decade?” and get the result. The platform using the NLP will convert the conversation into an actual query to get the result.
The NLP based analytics will grow and incorporate more complex queries to be executed in this decade. NLP, if implemented successfully, could change how data analytics is viewed and used dramatically.
With data growing ever so fast, the data analytics shows no signs of saturation. It is still evolving, and we will see some dramatic changes in the coming decade. The above mentioned are some of the trends likely to happen in the field of data analytics. With a variety of small to medium businesses starting to adopt data analytics, there is a number of possible ways the data analytics could be shaped in this decade.