Introduction
Since its inception decades ago, the primary objective of business intelligence has been the creation of a top-down single source of truth from which organizations would centrally track KPIs and performance metrics with static reports and dashboards. This stemmed from the proliferation of data in spreadsheets and reporting silos throughout organizations, often yielding different and conflicting results. With this new mandate, BI-focused teams were formed, often in IT departments, and they began to approach the problem in the same manner as traditional IT projects, where the business makes a request of IT, IT logs a ticket, then fulfills the request following a waterfall methodology.
While this supplier/consumer approach to BI appeared to be well-suited for the task of centralizing an organization’s data and promoting consistency, it sacrificed business agility. There was a significant lag between the time the question was asked, and the time the question was answered. This delay and lack of agility within the analysis process led to lackluster adoption and low overall business impact.
The emergence of self-service BI in recent years has challenged the status quo, especially for IT professionals who have spent the better part of the past two decades building out a BI infrastructure designed for developing top-down, centralized reporting and dashboards. Initially, this self-service trend was viewed as a nuisance by most IT departments and was virtually ignored. The focus remained on producing a centrally-managed single source of truth for the organization.
Fast-forward to today and IT finds itself at a crossroad with self-service BI as the new normal that can no longer be ignored. The traditional approach to BI is becoming less and less relevant as the business demands the agility that comes with self-service to drive adoption and improve organization outcomes. This, paired with the continued exponential growth in data volume and complexity, presents IT with an important choice.
Either the demand for self-service BI is embraced, and IT evolves to become the enabler of the broader use and impact of analytics throughout their organizations, or it is ignored and IT continues as the producer of lower-value enterprise reporting stifled by the limitations of traditional tools. IT professionals who are ready to serve as a catalyst and embrace this opportunity will deliver far greater value to their organizations than those who choose to ignore the real needs of their business users and analysts.
As organizations begin the transition from a traditional top-down approach driven by IT to a self-service approach enabled by IT and led by the business, a new framework and overall strategy is required. This means that past decisions supporting the core foundational
components of a BI program—people, process, and platform—must be revisited. Adjustments are needed in these three core areas to support the shift from a model of top-down BI development and delivery to a self-service-based modern BI model which is driven, and
primarily executed on, by the business.
People
Self-service analytics does not mean end users are allowed unfettered access to any and all data and analytic content. It means they have the freedom to explore pertinent business data that is trusted, secure, and governed. This is where process comes into play and represents the component that requires the most significant shift in traditional thinking for IT. A successful modern BI program is able to deliver both IT control and end-user autonomy and agility. A well-established and well-communicated process is required for an organization to strike this delicate balance.
A top-down, waterfall-based process only addresses the IT control part of the equation. A traditional BI deployment focuses primarily on locking down data and content with governance. This means limiting access and freedom to only a few people with specialized technical skills who are expected to meet the needs and answer the questions of the many. This typically involves developer-centric processes to design and build the enterprise data warehouse (EDW) model, build the ETL jobs to transform and load data into the model, construct the semantic layer to mask the complexity of the underlying data structures, and finally build the businessfacing reports and dashboards as originally requested by the business.
The unfortunate reality is that this approach often fails to deliver on the vision and promise of BI—to deliver significant and tangible value to the organization through improved decision making with minimal time, effort, and cost. Top-down, IT-led BI models often have an inverse profile of time, effort, and cost relative to the value they deliver to the organization.
A modern analytics solution requires new processes and newly-defined organizational roles and responsibilities to truly enable a collaborative self-service-based development process. IT and users must collaborate to jointly develop the rules of the road for their secure environment that each other must abide by in order to maximize the business value of analytics without compromising on the governance or security of the data.
IT’s success is highlighted, and its value to the organization realized, when the business can realize significant value and benefit from investments in analytics and BI. Should IT still be considered successful even if not a single end-user utilizes the BI system to influence a single business decision? Traditional processes intended to serve top-down BI deployments are too often measured by metrics that are not tied to outcomes or organizational impact.
If the ETL jobs that IT created ran without failure and the EDW was loaded without error and all downstream reports refreshed, many IT organizations would consider themselves successful.
Merely supplying data and content to users without any regard for whether or not it is adopted and provides value through improved outcomes is simply not enough. Modern BI requires updated processes to support self-service analytics across the organization. It also
requires the definition of new success metrics for which IT and the business are jointly accountable and are therefore equally invested.
Where processes and technology intertwine, there is tremendous opportunity. Technical innovations, especially with AI, will continue to make it easier to automate processes and augment users of all skill levels throughout the analytics workflow. And while process can
accelerate, rather than inhibit, successful analytics outcomes, it’s important to recognize that this relies on a system and interface that people are eager to use. If processes aren’t supported by the right platform choice, they will stifle adoption.
Platform
Since BI has been historically viewed as an IT initiative, it is not surprising that IT drove virtually every aspect of platform evaluation, selection, purchasing, implementation, deployment, development, and administration. But with drastic changes required to modernize the people and process components of a BI and analytics program, IT must change the criteria for choosing the technology to meet these evolving requirements. Perhaps the most obvious change is that IT must intimately involve business users and analysts from
across the organization in evaluating and ultimately deciding which modern platform best fits the organization and addresses the broad needs of the users. For more information on selecting the right analytics platform, check out the Evaluation Guide.
A modern platform must address a wide range of needs and different personas as well as the increased pace of business and the exponential growth in data volume and complexity. IT requires that the chosen platform enables governance and security while end users require easy access to content and the ability to explore and discovery in a safe environment.
The chosen platform must also be able to evolve with the landscape and integrate easily with other systems within an organization. A centralized EDW containing all data needed for analysis, which was the cornerstone of traditional BI, is simply not possible in the big-data era. Organizations need a platform that can adapt to an evolving data landscape and insulate users from increased complexity and change.
The most critical aspect is the ability to meet these diverse needs in an integrated and intuitive way. This integration is depicted on the following page as the modern analytic workflow. The diagram highlights the five key capabilities that must flow seamlessly in order for the three personas depicted in the center to effectively leverage the platform.
The BI and analytics platform landscape has passed a tipping point, as the market for modern products is experiencing healthy growth while the traditional segment of the market is declining with little to no net new investment. IT leaders should capitalize on this market
shift and seize the opportunity to redefine their role in BI and analytics as a far more strategic one that is critical to the future success of the organization. Adopting a collaborative approach to recast the foundational aspects of the BI program and truly support self-service is the key to changing the perception of IT from a producer to a strategic partner and enabler for the organization.
Promise
In today’s era of digital transformation, IT leaders are increasingly expected to take on digital business initiatives in their organizations, including identifying cost savings and finding new revenue streams. Realizing the value of data for these transformational efforts, many businesses are modernizing and increasing their analytics investments to innovate and accelerate change.
Everyone agrees that putting data at the center of conversations promises change. However, most organizations are failing to successfully implement an enterprise-wide analytics program.
IT is well positioned for a leadership role in these efforts, and is essential for the task of giving people the relevant data they need for decision-making. Modern analytics shifts IT’s role to a more strategic partner for the business, empowering users to navigate a trusted, self-service environment. But beyond access to the data, everyone needs the motivation and confidence to properly make decisions with it. You need to identify the relationships between job functions and data and change behaviors that run deep into the fabric of your organization’s culture.
This also means expanding your definition of self-service so that business users participate in some of the traditionally IT-led responsibilities associated with data and analytics—like administration, governance, and education. With the right processes, standards, and change management, business users can help manage data sources, analytics content, and users in the system, as well as contribute to training, evangelism, and the internal community. When users value and participate in these efforts, IT can manage strategic initiatives like business SLAs and ensuring the security of company assets.
Although every organization’s journey to building a data-driven organization will differ, achieving your transformational goals requires a deliberate and holistic approach to developing your analytics practice. Success at scale relies on a systematic, agile approach to identify key sources of data, how data is selected, managed, distributed, consumed, and secured, and how users are educated and engaged. The better you understand your organization’s requirements, the better you will be able to proactively support the broad use of data.
Tableau Blueprint provides concrete plans, recommendations, and guidelines as a step-by-step guide to creating a data-driven organization with modern analytics. We worked with thousands of customers and analytics experts to capture best practices that help turn repeatable processes into core capabilities to build and reinforce a data-driven mindset throughout your organization.
Learn more and get started today.
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