The Good The Bad And The Ugly Of Enterprise BI
Unified information architecture, data governance, and standard enterprise BI platforms are all but a journey via a long and winding road. Even if one deploys the "latest and greatest" BI tools and best practices, the organization may not be getting any closer to the light at the end of the tunnel because:
- Technology-driven enterprise BI is scalable but not agile. For the last decade, top down data governance, centralization of BI support on standardized infrastructure, scalability, robustness, support for mission critical applications, minimizing operational risk, and drive toward absolute single version of the truth — the good of enterprise BI — were the strategies that allowed organizations to reap multiple business benefits. However, today's business outlook is much different and one cannot pretend to put new wine into old wine skins. If these were the only best practices, why is it that Forrester research constantly finds that homegrown or shadow BI applications by far outstrip applications created on enterprise BI platforms? Our research often uncovers that — here's where the bad part comes in — enterprise BI environments are complex, inflexible, and slow to react and, therefore, are largely ineffective in the age of the customer. More specifically, our clients cite that the their enterprise BI applications do not have all of the data they need, do not have the right data models to support all of the latest use cases, take too long, and are too complex to use. These are just some of the reasons Forrester's latest survey indicated that approximately 63% of business decision-makers are using an equal amount or more of homegrown versus enterprise BI applications. And an astonishingly miniscule 2% of business decision-makers reported using solely enterprise BI applications.
- Business-driven homegrown shadow BI is Agile but not scalable. Successful businesses are now focused on having fast reaction times, a bottom up, grass roots approach to information, tolerating a higher level of risk in exchange for near real-time information and empowering business units with the information they need to best serve the customer. When enterprise technology management can't deliver, business users build their own applications focusing on agility, flexibility, and reaction times (being reactive is a good thing in the age of the customer). Alas, these noble efforts by non-technology professionals have their own set of challenges, and often result in applications and environments that do not scale, contribute to the proliferation of silos, take organizations farther from a single version of the truth, and pose high operational risk.
Unfortunately, this results in an ugly reality for BI strategy and the BI journey in many organizations: Business and technology stakeholders are disconnected and use different platforms, and true business requirements and priorities are lost in translation. The result is a lose-lose-lose where customers don't get what they want, and business and technology pros spend too much time arguing over and supporting their own agendas, and often feel like they've reached a fork in the road and must go their separate ways.
It's clear that neither approach — technology-driven enterprise BI, nor business-driven homegrown shadow BI — is enough on its own in modern 21st century organizations. So how does an organization have the BI cake and eat it too? How does an organization balance business users' need to produce their own BI content with little dependence on complex technology processes and infrastructure, while at the same time minimizing enterprise risk, achieving economies of scale, and getting rid of silos? The answer lies in business-driven Agile enterprise BI.
Forrester defines Agile BI as an:
Approach that combines processes, methodologies, organizational structure, tools and technologies that enable strategic, tactical, and operational decision-makers to be more flexible and more responsive to the fast pace of customer, business and regulatory requirements changes.
Going well beyond just a definition, in our latest research we identify four key components of Agile BI
- Agile BI software development methodologies
- Agile BI organizational structures
- Agile BI processes
- Agile BI technologies
Please read the full report for more details and best practices for each of the Agile BI components.