This February, Gartner announced a somewhat unexpected change in categorization language in their 2016 BI (Business Intelligence) & Analytics Magic Quadrant by introducing the concept of “Modern BI Platforms.” This shift sent ripples through the BI platforms space as vendor positioning shuffled, leaving Microsoft, Tableau, and Qlikview as leaders when the dust settled.
Why the Change? Gartner identified changes in buying patterns as the primary catalyst for the market perspective shift. Customers searching for a Modern BI solution are looking for data wrangling capabilities, ease of data exploration, and visual storytelling abilities. Users of what has been deemed Traditional BI have very different needs of guided report usage (often operational reporting), scheduled report delivery, and cross-domain reporting of large, aggregate datasets.
While BI refers largely to the presentation layer, this shift to Modern BI also introduces a change in the way organizations are using data to make decisions – what we at West Monroe refer to as the Modern Data-Driven Organization. Organizational spend across the globe is increasing for self-service data ingestion, and advanced business analysis tools such as Tableau. But before handing out Tableau licenses and calling it a day, it’s important to understand these five keys to successfully grow a Modern Data-Driven Organization.
Key #1: Drive a Culture of Data-Driven Decisions
Data! Data! Data! I can’t make bricks without clay!
– Sir Arthur Conan Doyle
Data is one of the most powerful assets for organizations, but all too often we see access to that asset limited to a select few, often a combination of leadership and IT personnel. Building a culture of data-driven decision-making through data democratization introduces opportunity for informed action at all levels of the business. Democratizing data is a popular expression today – explained as releasing data to the organization that has traditionally been gated through IT departments. It can seem scary to companies with previously guarded attitudes toward data, but is key in emphasizing the importance of informed decisions at every level.
Key #2: Empower Business Users
The value of an idea lies in the using of it.
– Thomas Edison
With a culture of enabling data exploration at all levels, giving business users the right tools for the job and educating them on the business interpretation can make all the difference. Tableau, Qlik, and Microsoft Power BI shine in marketplace full of user-friendly data analysis tools. Securing a tool that can simplify data wrangling (gathering, relating, and cleansing data) and visualize meaningful insights with little technical training is important to getting accessible insights to the end user quickly and accurately.
Key #3: Enable Cross-Domain Analysis
For me context is the key – from that comes the understanding of everything.
While the simplicity of Modern BI is attractive for business verticals, these practices fall short of delivering contextual analysis across domains. This type of analysis can be achieved by centralizing data from disparate sources into a logical Data Model or physical Data Warehouse. While often deemed “traditional,” standardizing data centrally helps organizations reduce redundant efforts, reconcile results, and report across business verticals. Looking at the organization as a whole leads to better understanding of cause and effect and untapped opportunity between functional areas.
Key #4: Enrich and Extend with Analytics
There are Lies, damned lies, and statistics
As Modern and Traditional BI help analysts explore “what happened?” Advanced Analytics can help Data Scientists analyze further to predict, test, and expand data use practices. Scientists hypothesize, research to understand patterns, with the ultimate goal of determining how things work. Data Science is no different. Common use cases include standard segmentation of customers, forecasting calculations, and understanding key drivers for results. This information can be used as an input to the use of data in other areas of the organization.
Key #5: Measure ROI and Drive Standards through Governance
I look at Google and think they have a strong academic culture. Elegant solutions to complex problems.
To be effective, this culture must be empowered with standards and guidance – called Data Governance. Following this Data Democracy theme, Data Governance should be representative of perspectives from all three types of analysts, along with leadership across business verticals. This Governance Entity should be solely responsible for measuring results, defining new data use standards, and ultimately driving towards a better product. Governance is the central force to iteratively improve the Modern Data-Driven Organization and without it results soon become, well, something less than modern.