Overview of BI Implementation
Implementing Business Intelligence isn't rocket science but it takes meticulous preparation and following the step-by-step process:
1. Develop a business intelligence implementation strategy: Without a clear strategy in place, employees make assumptions about unclear business terms and definitions, use different data, and adhere to contradicting instructions. What makes matters worse is that data quality is only considered incidentally during such random and unplanned efforts. While creating a roadmap, it is imperative that you answer these fundamental questions - What does your business have? What’s your end goal? What technology/resources do you need?
Make a list of the departments in charge of storing the data and the data that is currently available. Then, understand whether you will require additional data and what resources must be employed to acquire it. It is important to make a note of where you would use BI insights derived from data and how will you shape your business objectives. By setting objectives and using data smartly, you will be able to reach goals in an efficient way. Further, form a vision of what your future BI system will be like - whether it will integrate the existing structure or replace it. Only after you have built a sound BI implementation strategy, you can go further.
2. Appoint professionals to carry out BI implementation: Once the strategy is developed, you must choose a team of professionals who will carry it out. The implementation team should include representatives from every department because business intelligence is essentially a cross-departmental problem rather than merely an IT or financial initiative. Employees involved in the project who work with business data should also be included. In order to overcome traditional resistance to change, the whole staff of the organisation should be carefully informed of how each individual will profit from the BI implementation.
3. Define relevant KPIs: Examine the business objectives and pain points of your organization to determine which KPIs are crucial, which are highly effective, and which can be eliminated. Organize KPIs into categories to speed up the hiring process. Some examples include project management metrics (ROI and productivity), financial indicators (net income, sales growth, liquidity ratio), marketing data (customer acquisition cost, cost per lead, conversion rate), customer metrics (social media traffic, customer lifetime value, monthly number of new customers), and HR indices (cost per hire, net income per employee). Knowing which KPIs you would use to measure performance throughout the organization also holds true significance.
4. Find a reliable software provider: Modern businesses rely significantly on IT, therefore, using the right software solutions is essential to integrating business information. Of course, you can try to handle it yourself and put your trust in the internal team. In this instance, it should include a project manager, a data quality analyst, an application lead and ETL lead developer, a data mining specialist, a data and database administrator, and a project manager.
5. Select the right BI technology & tools: There are a variety of business intelligence technology & tools available nowadays, therefore, in order to choose wisely from a pool of options, it is important to consider your functional needs and non-functional requirements (security, performance, availability, etc.). Your decision is greatly influenced by how you obtain the data, visualize it, and interact with the metrics. Out-of-the-box solutions will work if your data intelligence activities are going to be of medium scope. However, if you intend to delve deeply into analytics, think about investing in specialized tools that might be properly matched to your vision of business intelligence. However, you can avoid the hassle of selecting BI tools by giving the chosen vendor complete control over all technical aspects, which is usually the best course of action.
6. Consider infrastructure: Data storage and the BI platform are the two essential infrastructural components connected to putting business intelligence projects into action. On-premise data banks are viewed as outdated & cumbersome that can hardly keep up with the massive data volumes of data and ever-changing demands placed on data management today. Because of this, the majority of businesses choose enterprise data warehouses (EDW), which can effectively handle and store large volumes of data from numerous systems and applications (such as ERP, CRM, and HRM). Local BI platforms are also out of date and are progressively being replaced by cloud services. Try hybrid solutions that incorporate the best of both worlds as a balanced tradeoff between the level of control and storage cost provided by either.
7. Data Preparation: The data preparation process consumes around four-fifth of the time BI development requires. Data silos, a phenomenon where separate departments may utilize distinct tools, techniques, codes, storage patterns, etc., should be addressed first. There are other data quality metrics to consider once data integrity has been attained (completeness, validity, consistency, relevance, accuracy, uniqueness).
8. Initiate a feedback loop: The current status project status should be evaluated, modifications (if any) should be implemented at regular intervals, and schedule meetings of the BI stakeholders.
9. Start small: Launching a trial with a few key KPIs is highly recommended. Collect feedback on its effectiveness and any identified bottlenecks to improve the overall plan (if required).
10. BI Implementation on a larger scale: After ensuring that the necessary adjustments have been made and your approach is effective, proceed to integrate business intelligence with additional KPIs. To complete the project and provide the desired results, keep in mind to evaluate the process frequently and optimize the problematic areas.