Data-driven decision-making is a critical aspect of any digital transformation initiative. Success hinges on rethinking BI and dispelling commonly held beliefs about data and analytics accessibility.
The thought of business intelligence (BI) and analytics conjures up images of business leaders and managers peering into colorful graphs and charts, orchestrating data-driven strategies, and driving innovation. However, the reality is much different. Self-service business intelligence is still theoretical for most enterprises; technical skills are still required to create dashboards and perform analysis, and systems are still too fragmented, causing mistrust in data and chaos in decision-making.
Nearly all organizations prioritize business intelligence and data analytics, yet most struggle with transforming that data into insights and – arguably more importantly – empowering the people within their organizations that need it most: the decision makers. A recent study by 451 Research found that almost one-third (32 percent) of companies have yet to fully embrace a data-driven approach to strategic decision-making. Organizations still have a long way to go on their data journey. While 90 percent of respondents said data will be more important to their organization in 2022, the report made clear that a sizable number of organizations are missing out on the benefits of utilizing data as a decision-making tool.
Why the disconnect?
Despite BI tools claiming to be ‘self-service,’ business leaders frequently need support from data scientists and analysts, dragging productivity and adoption rates. Today, most analytics tools are made for technical users, not managers or even executives, and within many organizations, there’s a mismatch of tools spread across departments or projects. The result is delayed time to insight, slow adoption, and high costs and complexity – all stemming from having multiple tools for data prep, business analytics, and data science. Inconsistent KPI reporting and too much time spent questioning data accuracy further erodes effective and efficient decision-making.
If these challenges weren’t enough, there’s another issue at play here: misconceptions born from the limitations of today’s BI tools and wrongful assumptions about the people that manage and use BI and analytics.
When it comes to empowering your organization’s most valued resource – your people – with data-driven decision support, here are three common myths that need to be dispelled:
Myth 1: Generating reports is IT’s job.
The IT team has traditionally taken the leadership position in the evaluation and selection of new tools, as well as implementation, integration, and training staff on the new tool. When it comes to business intelligence, organizations have no shortage of BI tools at their disposal, but many find people lack the skills to leverage these tools for basic reporting, let alone using them for strategic decision-making. In these scenarios, the IT team is asked to generate reports and make further changes and iterations on a regular basis. This routine practice delays the availability of timely data and disempowers people from embracing and fully leveraging their organization’s data.
Instead, let your IT team focus on their core mission, not on building reports. Your IT team brings significant value as administrators and managers of the data and analytics stack. They are in charge of balancing data security and access and optimizing infrastructure investments to manage costs. Why distract them from their core mission by having them spend countless hours manually producing and reproducing reports? The creation and upkeep of the reports should be with the business, the place where decisions are made.
Analytics can live where decisions are made. Decision intelligence platforms combine data preparation, business analytics, and data science with an AI-driven, governed self-service experience. These next-generation platforms feature direct query engines that access data where it is stored. This delivers answers at speeds that in-memory database queries cannot while eliminating the security risks of downloading data to desktops. Best of all, these new capabilities allow non-technical business users to be self-sufficient, accessing and preparing data for analysis independently without having to wait for IT.
Myth 2: The more data scientists you have, the more value you derive from data.
Data science is an exploding field fueled by organizations seeking actionable insights from the vast amount of disparate data they have collected. The U.S. Bureau of Labor Statistics predicts the data science field will grow by about 28% through 2026. Hiring data scientists is often viewed as a critical first step. However, many organizations aren’t prepared to accommodate the specialized environments they require. What’s more, organizations shouldn’t expect data scientists to use business intelligence tools. So, despite an urgent need for more advanced capabilities, hiring data scientists may only make it more difficult to reap the benefits of data investments.
The reality is citizen data scientists are already within your organization and can be empowered with self-service analytics. These citizen data scientists, armed with domain knowledge and a strong desire to use data to drive their decisions, can, in some cases, remove the need to hire pedigreed data scientists. In many cases, they can be just as effective as data scientists, as long as they have access to a solid foundation of data and analytics capabilities – like those made possible by decision intelligence platforms.
Myth 3: The adoption of data-driven decision-making will never grow beyond people with technical skills.
Despite the powerful potential and colossal investments made in business intelligence tools for self-service analytics, the actual usage of these tools is far less than anticipated. What’s more, while most companies understand the importance of analytics and have adopted common best practices, fewer than 20 percent, according to a recent McKinsey survey, have maximized the potential and achieved advanced analytics at scale. Common challenges impacting the scalability and adoption of BI tools include tools that are simply too challenging and time-consuming to use and an overall lack of appropriate analytical skills required to effectively use the organization’s chosen technologies. As such, it is well-accepted that only a select few within an organization actually “own” the business intelligence function. These challenges lead to low adoption rates. According to The BI & Analytics Survey 23 from independent research and advisory firm BARC, the average adoption rate of BI/analytics tools remain stuck in the 20 percent [HR1] range.
The widespread adoption of analytics for optimizing decision-making is indeed possible on a platform that leverages AI to enable governed self-service experiences that deliver the full breadth and depth of business analytics functionality for all business user types. Governance is a key factor in making data-driven decisions efficiently and responsibility, and therefore should permeate analytics tools, platforms, and processes. By centralizing data access in a governed location, and by giving anyone — regardless of skill — governed, self-service analytics experiences that adapts to their needs, an organization can scale analytics beyond the few to the many. Decision intelligence can be extended to all people in the organization to make better decisions in their unique roles.
In summary, data-driven decision-making is a critical aspect of any digital transformation initiative. As covered here, investing in decision intelligence platforms can empower your organization’s most valued resource – your people. However, success hinges on rethinking the approach to BI and dispelling commonly held beliefs about data and analytics accessibility. Decision intelligence is what’s next in analytics.