Should You Automate Business Intelligence?
Updated · Oct 06, 2015
By Heine Krog Iversen, TimeXtender
Business intelligence can deliver great value because it allows people to get reliable feedback on complex action patterns. These actions can happen on an individual level or across departments, roles and teams. When properly used, BI can help anticipate and safeguard against undesirable business outcomes and can even allow for early warning systems from environmental factors like an economic downturn.
Some business questions and answers are predictable – which is why standardized business intelligence solutions can mean such a big leap forward for a business. Companies invest lots of time and money to build and deploy a data warehouse and business intelligence system, but then they often get stuck right after that.
As they progress, their questions become more profound and more complex to answer and can be outside the realm of the initial system. Making sound and swift strategic decisions are crucial for a business in today’s marketplace. This is why a BI endeavor must be able to deliver needed standard intelligence fast, and complex intelligence even faster.
This is where automation comes into play. Automating your data warehouse can accelerate, simplify and improve BI’s ability to answer standard and non-standard business questions, resulting in faster implementations of solutions and providing insight into patterns and trends as they develop.
OK, you have made the decision to automate your BI. Before beginning, it is essential to understand that we’re not striving to build new business intelligence components, but to build a replacement for an existing element. For example:
- A customer/market dashboard to replace individual “gut feeling” spreadsheets
- A corporate MIS (management information system) portal to replace departmental sheets and reports
- A new set of KPIs (key performance indicators) to accommodate the change in strategy
- New interactive and data visualization dashboards to replace business solution modules
- Data warehouse automation instead of manual, hand coding
Automating Business Intelligence: Where to Begin
Where to start? To keep it simple, start automating what you already have. While that sounds easier said than done, it is. To begin, break down the work in doable chunks and begin by asking, “What is the best chunk to start with to automate?” Then, recognize that most BI methods work with some form of iteration such as Agile methods.
Next, perform an audit by asking the following questions:
- Which departments and what roles are we building this for?
- Is this an add-on to existing work?
- How many people will benefit from this?
- How hard is the task at hand? How much time will it take? Who needs to get involved?
- Do we have the data?
- Do we have a clear idea on what to build?
Let’s consider some of these questions more fully.
Getting BI Automation off the Ground
What is it we are trying to achieve in building this user story? What do we hope to address in answering the BI questions?
For example, would we like to get a clear view on marketing spend and effectiveness? We want to measure differences between regional offices (marketing spend/number of new or returning customers per region) or campaign efficiency per customer group.
For whom are we building this? Which roles and responsibilities, how many people will (potentially) be using this?
For example, will this be used by regional managers and local business development teams, as they are both responsible both for deciding on campaigns and marketing spend?
How does the question break down in building blocks: measures, dimensions and output components such as reports, dashboards and websites?
Examples include: a customer hierarchy per geography, a customer per category, a product hierarchy per category, a marketing campaign hierarchy per territory, product category and customer group. These dimensions would allow us to measure margin, rate of return, customer average spend, marketing budget, sales budget, goals and KPIs. For visualization, we would likely have an online platform or website, accessible via smartphone, tablet and PC, with easy access to retrieve hard copies for meetings and presentations.
What is it that we need to replace?
We want to avoid extra work and costs which could yield little in return. For each new element we create, we risk creating double work. Double work depletes money and resources. By considering what it is we are likely going to replace, we want to make sure we are not adding a variation to a theme already existing in the system.
We want to get extra return at little or no extra cost. We need to identify people who might benefit from the same work. After all, as the original system came from somewhere; it must have been serving some people well at some point in time. The new user story may serve a new target group, but did the old system serve the same group of people? Do we have an opportunity to expand our BI audience with minimal extra work?
We should maintain focus and ensure that some needs aren’t so specific for a small group of end-users that they draw precious resources with very little payoff. In this scenario, another option should be formulated and developed outside of the main system.
Create an overview of user groups or stakeholders, gradually add the structure of the decision support system, domains, reports, dashboards, scorecards and data sources, and evaluate vis-a-vis the parameters and questions posed as stated above.
In business intelligence change is constant, and any type of change can have dramatic impact on work and rework. However, most of the work is about changing details or creating small variations. Using Agile methodology to fulfill the iteration agenda, data warehouse Automation allows business intelligence teams to focus energy on the harder tasks at hand, and to automate small change issues fairly easily.
The end result for companies is a faster, more robust BI program that can answer the questions you sought out to answer when you made the technology investment. And it will likely provide needed intelligence for your business that you didn’t know existed.
Heine Krog Iversen is the CEO of TimeXtender, the largest provider of data warehouse automation software for the Microsoft SQL Server. Heine oversees a global organization and worldwide customer base of more than 2,600 customers. He can be reached at [email protected].
Paul Ferrill has been writing for over 15 years about computers and network technology. He holds a BS in Electrical Engineering as well as a MS in Electrical Engineering. He is a regular contributor to the computer trade press. He has a specialization in complex data analysis and storage. He has written hundreds of articles and two books for various outlets over the years. His articles have appeared in Enterprise Apps Today and InfoWorld, Network World, PC Magazine, Forbes, and many other publications.