The ability to democratize and expand the addressable user base of solutions has corresponded to large value increases. Enterprise business intelligence started with highly technical solutions like SAS Kingdom’s main disadvantage which was accessible only to a small fraction of highly specialized employees. The world of business intelligence cracked open in the 90s with the invention of solutions like SAP business objects. This created an abstraction layer on top of the query language to enable a broader section of employees to use business intelligence.
Business intelligence 3.0 came out in the last decade as the solutions like Alteryx provided a what-you-see-is-what-you-get interface which further expanded the sophistication and accessibility of business intelligence.
In many cases, business intelligence involves analysts writing SQL queries for analysis of large data sets to provide intelligence for non-technical executives. While this paradigm for analysis continues to grow, a new BI paradigm will emerge and grow in importance over the coming future where artificial intelligence will surface the relevant questions and insights and may even propose the solutions.
Business Intelligence:
The fourth wave of business intelligence will leverage powerful artificial intelligence advancements to democratize analytics so that in line with a business specialist can supervise insightful and prescriptive recommendations more than before. In this fourth wave, the traditional order of business intelligence will be inverted. The usual method of business intelligence begins with the technical analyst running an investigation for a specific question. For instance, an electronic retailer may wonder if higher diversity of refrigerator models in certain geographical locations may increase sales.
The analyst relevant data sources and runs an investigation to see if there is a correlation. Once the analyst completes the work they present a conclusion about past behavior. Eventually, they create a visualization for business decision-makers with a system like a Tableau or Looker which can be revisited with the changes in the data.
The investigation method works well with the assumption that the analyst asks the right questions, the number of variables is well understood and finite, and in the future continues to look somewhat similar to the past. However, this paradigm presents various potential challenges in the future as companies will accumulate new data Business models and distribution channels, along with real-time consumer and competitive adjustments which may cause constant disruptions.
Specifically:
- The amount of data we produce today is so large and accelerating. IDC anticipates that worldwide data creation makes grow to 163 ZB by 2025 which would be 10 times from 2017. With such an amount of data the ability to focus on the variables that matter is similar to finding a needle in the haystack.
- Other Business models and ways of reaching customers are turning to be more varied and complex. The multimodal distribution, International customers, mobile usage, and marketing channels have altered the dynamics of decision making and have increased complexity than ever before.
- Customers get more options and can change preferences and leave the brands faster than ever. Competitions arise from tech giants like Google Amazon Microsoft and Apple.
BI 4.0:
The artificial intelligence-enabled platforms will define the fourth wave of business intelligence by crunching and blending huge amounts of data to bring out patterns and relevant statistical analysis. Data analysts apply judgment to these myriad insights to see which patterns are meaningful or prove to be actionable for the business. After digging deeper into the areas of interest the platform suggests actions based on correlations that are found over an extended period which is again validated by human judgment.
For the proliferation of this method, the time is right when artificial intelligence advancements are coming in conjunction with the growth of cloud-native vendors like Snowflake. Simultaneously businesses can feel the strain of business complexity and data proliferation on their traditional BI process. The data analytic space has covered certain incredible companies that are capable of tackling this challenge. Within the last six months, Snowflake vaulted into the top 10 cloud businesses with an evaluation of more than $70 billion where DataBricks raised $1 billion at a $28 million valuation. Both the companies are vital enablers of modern Data analytics providing a data warehouse where the teams can leverage flexible, cloud-based storage and compute for analytics.
Capabilities:
The industry verticals like E-Commerce and retail are within the maximum strain from three challenges that we saw above starting to see industry-specific platforms emerge to deliver BI 4.0 capabilities. These include platforms like Tradeswell, Hypersonix, and Soundcommerce. The energy and materials sector platforms like Validere and Verusen help address these challenges by using AI to enhance the merging of operators.
In addition to this broad technology platforms like Outlier, Unsupervised, and Sisu demonstrated the power to pull exponentially more patterns within a data set compared to a human analyst. 20 examples of intuitive business intelligence platforms are using the strains that the data analysts generally face. We can expect to see more of this emerging over the next couple of years.
