Does Your BI tool give the desired output? Reasons of Failure and Solutions

Business intelligence is the order of the day. Its healthy incorporation leads to rapid growth and development in one’s business. BI helps in capitalizing business data and strategically implementing it. Considering how businesses are creating massive data, it is important to implement effective BI.

A lot of companies fall back in the right implementation. Over the period of time, a lot of case studies have surfaced which clearly reflect on the poor utilization of BI. The logic is to assimilate the master data, enterprise information and required systems to manage them in the order. As much 50% of projects usually fail. Why do projects fail? Some of the vitals factors that lead to the failure of any BI project include:

• Data transformation – A lot depends on how the data is being shaped or molded as. If there is an error in treating data the right away then there are fair chances for BI failure. Data is required to be consolidated, cleansed, and integrated.  Synchronizing the data from other heterogeneous sources is important factor as well.
• Swamp and usable data – In the recent times some challenges that have surfaced wherein it has been noticed that data not being segregated. It is important to assemble and summarize the historical data and transform it into a real-time decision making medium. It is critical to differentiate between swamp and usable data. This is where an effective BI strategy comes into picture which helps in segregating the data using the right tools.
• Cubes and dimensions – One of the major reasons why BI projects fail is when the cubes and dimensions are not identified and defined. For ideal defining, one should acquire valid data which is easier to extract. A lot of companies fail in this section as they do not define it very well. So, if a small entity in a dimension goes wrong then everything will go wrong. At the same time, writing query i.e Schema is required to be defined properly and further used to extract data.
• Functional expertise– It is important to analyze how qualitative is the data. Often dirty data is responsible for a ruined project. If the data is of poor quality, even any major data warehouse will not be able to render desirable results. It is important to have full knowledge of the kind of data you have and the required methodologies, frameworks, processes and technologies. BI tools like Kettle; Spoon etc can be major assistance in this regard.
• Escalated timelines – Usually BI projects take approximately 18 to 24 months of time to bring out a meaningful analysis. By the time the real analysis takes place, the business relevance is lost. This leads uncertainty leads to degradation in the quality. The time period is invested in churning the right data, produce the right reports, dashboards and cubes. It is important to go for a highly skilled and technically abreast BI team which understands the core issue. It is extremely important to monitor the BI strategy and the business need. When you have the right team and analytical tool, getting data within days or weeks is an accessible task.

When it’s business intelligence, the most crucial aspect is data validation. It makes it easier to trace out the error and work on it right away. Further, drilling down even the smallest entity is well recommended to find out if there are any errors. Lastly, a lot depends on the kind of reporting type you are choosing. With clear report presentation, it’s easier to bring in the information.

HyTechPro has successfully executed BI projects in different verticals like ecommerce, healthcare, housing and others. Let’s talk about one of our clients who was facing problems where a lot of time was already invested with zero results. The main problems majorly arose in areas like validating data and cubes defining. After, we got together in partnership, we along with our BI team reduced considerably on time. Within three weeks, we were able to deliver reports.

Although the use of real-time information in a Big Data environment represents a significant advancement, the challenge is to look at the data from a longitudinal perspective in order to get the historical view of the customer. It is this longitudinal view of the data that allows us to conduct the more advanced type analytics and to ultimately develop predictive analytics solutions.

At HyTechPro, we base our work on analyzing this longitudinal view of the data first along with our clients. We do believe that unless the data is churned and analyzed properly, any BI or Big Data project would be a huge failure. Our experienced data scientists do this rigorous exercise with the clients so that business objectives are clear and data swamp is cleared before the real data is being worked upon. This is what sets us apart from other companies who are just more technology focused and end up in building a huge tech savvy product which is nowhere close to what business desired from them. This is the real essence of Big Data as real-time offers and actions can be presented to customers based on predictive analytics solutions rather than the current data point or activity.

Have views on BI? Wish to share? Get in touch with me.

Share your views at – askus@hytechpro.com