Business users still not drive business analytics deployments
Let us make no mistake: technology and architecture need to be heavily involved in deploying analytical platforms. If they aren’t we will quickly face issues on scalability, governance or even operational risks. But if they are involved properly, the sole drive of a business analytics platform is definitely adoption.
So, why is adoption-rate the key driver for having business involved in your business analytics deployments? Well they are our biggest “customers”, aren’t they? If the analytics provided does not have the content, functionality and accuracy that the users require to run their business, they simply won’t use it. They would go and find their own trusted sources to get the insights they need. And still business is not always being involved in the analytics deployments. Still the majority of the deployments is solely technical driven. How come?
Conflict of interests
In most of the cases I have seen, there seems to be a conflict of interest between the technology-driven side of business analytics environments versus the business-driven side of it. We reside in a world of Digital Transformation. A world with networked economies and networks, with almost unlimited access to information. Information we need at our fingertips to be competitive and use business analytics as competitive weapon. It means we need to adapt to change if we want to use that weapon. Business people are the ones who are directly in touch with their customers and the market, and as such can best judge challenges and opportunities. Responses to change therefor best are driven by business people. To do so, they require their organization to be extremely agile, especially for the analytical environment.
Opposite are the technology-driven forces within companies. Their interest for the analytical environments is about standardization of platforms. It is also about data governance and ensuring everybody is using the “same truth”. They stay awake to manage maintainability, risk and support of the platforms. And last but not least, the technology-driven forces look for scalability. In a sense their interests prevent the platforms to be agile at all. Though respecting the rules of proper system development, it is not a very effective approach in an era of transformation and change.
Voila, our conflict of interest is here. Business people talking to customers and interacting in their markets, require agile analytical platforms to be able to go through the change of Digital Transformation. Opposite are the technical-driven forces who provide scalability but definitely not agility. What direction do we need to go and is there a way to align both interests?
The focus needs to be on agility
It is quite obvious that organizations – if they want to remain competitive – need to change and conform to Digital Transformation. Therefor the technical-driven people should embrace the following concepts to their analytical platforms
Our internal business users are “the customers” of the analytical platforms and need to be
treated as such. Business users interact with markets and real customers and are really best suited to judge what is needed, when and why. They should in return be responsible to incorporate their new insights into the analytical applications, making the applications more intelligent
Allow the business users to be agile by providing them with analytical solutions that are agile in itself. Solutions that are extremely flexible, easy to use and with very low adoption curves. Allow the same business users to create their own content with limited dependence to on complex infrastructures
Accept that the required agile responsiveness to continuously changing data and sources, means we have to accept a certain level of freedom in when it comes the single truth provided by the platform.
Join forces and align both interests: a Customer-Centric approach
The above narrows down to a discussion on scalability versus agility. A discussion where mission-critical, robust and governed platform-solutions need to compete with flexible, agile and responsive analytical platforms. Can we bring both worlds together and join forces?
Yes we can: the answer is in a customer-centric approach by both parties. Technology-driven people must see the end users as their customers. It opens the door to an business-driven Agile enterprise analytics platform, composing of following 3 core elements:
1. Agile Business Intelligence software components
Rapid proto typing: provide your business users with the right tools to quickly generate valuable prototypes. Have a look at my BI Component Selection tool to get some guidance
Review the process of business requirements: in my experience the process of gathering business requirements and setting priorities takes way to long and is too complex. The world around us is changing so rapidly, we cannot afford to have this process taking too long. Read some tips here how to shorten this process
Tangible results within a few weeks: we must use BI software components that allow us to deliver tangible results in weeks to meet agile objectives
2. Agile Business Intelligence Organizations & Processes
Flexible and business driven BICC’s: Business Intelligence Competence Centers need to be virtual and positioned cross-departmental. They are led by business people but have a vast amount of technology-driven resources embedded
Lead by example: BICC’s are the core drivers in creating valuable prototypes. They lead by example using and showing best practices on agile BI developments. Prototypes that are being successful are promoted to be embedded in the corporate analytical platforms
Interactive organizations working with interactive data: With a customer centric approach both business users and technology resources interact mutually on a permanent scale. They apply the mechanisms of interactive-insights-gathering
3. Agile Business Intelligence platforms
Platforms that apply the Trusted Data Discovery (TDD) principle: In a business driven Agile enterprise analytics platform we need managed reporting, managed dash boarding, sell-service BI and probably also predictive analytics capabilities. By all means we need one single platform with centralized governance and metadata definitions. Many, many vendors generate silo’s for each of these disciplines which is just not acceptable. Being agile is also about having a robust and scalable core with centralized governance. A very good example of a platform that respects TDD is the SAP BI Suite.
In-memory computing is a “must”: Just to process and interact with the information that comes all over us with Digital Transformation, in-memory computing is a sine qua non.
To be competitive, organizations should transform to a customer centric approach aiming at a business-driven agile enterprise analytics platform. To tackle the traditional conflict between scalability versus agility, analytic deployments must be approached over a 3-tier agile approach on agile platforms, agile software and agile processes.