By Ajay Anand
Times are changing. Is your business still set up for business intelligence (BI) success with your current tools, people, and processes? Collecting data is one thing, but are your business users getting value from all the data your enterprise is generating? If not, how do you transform your BI stack for greater success?
Enterprises have vastly expanding data lakes thanks to new information from the Internet of Things (IoT) to real-time data streams being added constantly. Yet, many companies today aren’t getting the value and insights from their data lakes as they initially anticipated.
While deploying Hadoop provides a cost-effective way for users to store massive amounts of information, trying to derive insights from petabytes of data often leads to poor results if the corresponding BI infrastructure is not equally robust. This fact was painfully clear in a survey of 250 enterprises that Kyvos conducted recently. We found that only 27 percent organizations say their BI infrastructure measures up in terms of response times or at the fine granularity needed to reveal actionable business insights – let alone democratize the use of BI or deliver the return on investment.
The key success factor for big data implementations is its adoption by business units and ROI. Business users are looking for self-service, interactive access to their big data without IT dependency. However, their traditional tools can neither scale or perform on such massive amounts of data.
Each industry or organization has its own reasons for using business intelligence. Whether it’s a supply chain manager collecting data from millions of devices to streamline their field processes or a marketing professional gathering customer data to learn about customers’ issues and behaviors, BI can help optimize business processes at every level, but for that a robust BI infrastructure must be put in place.
Where to Start?
Business intelligence projects need to be set up properly from the beginning in order to achieve optimal success. The most critical step towards evolving BI is to understand its objectives. Also, it’s important for businesses to move beyond previous BI standards and plan what they really want to know. These insights might span different sources of data or even different kinds of data, and to be successful it’s necessary to consider how to keep a business ahead of its competitors.
Here are a few crucial things for businesses to keep in mind when embarking down the modern business intelligence path:
Business Goals
The most important thing is to consider the business goals and objectives for the data. It’s easy to get caught up in the process and lose sight of the purpose. If wrong data is being collected, or it’s being organized in a way that doesn’t support the end goal, the results are going to be disappointing or useless.
Agility
In business, change is a constant. Your business intelligence system needs to be able to adapt quickly to a changing business environment. Factoring that up front will help you be ready to respond quickly to a shift in tactics. Make sure the systems you put in place maintain high levels of performance with your exponentially growing data volumes. This includes ensuring that business users can interact with the data as needed and still get instant response times – even when hundreds or thousands of concurrent users are involved.
Data Quality & Structure
Data is the foundation of business intelligence. Never has the term “garbage in, garbage out” been quite as fitting as when talking about the data being imported into a BI system. Establishing strict guidelines is important to help ensure data is accurate, clean and consistent.
It’s also important to understand the semantics of the data. Organizing and structuring it is a necessary step to ensure business users are viewing the data correctly and are understanding the value it’s providing.
Self-Service
Ease of use is critical for BI projects to succeed. If business analysts have to rely on IT to pull reports, the entire process just slows down. And that is compounded when the analyst needs to learn a new tool and break away from their familiar analysis environment. Seamless connectivity to their existing BI tools allows analysts to explore their data and identify the hidden patterns.
In Conclusion
Don’t be limited by what you would like to achieve based on previous perceptions of what is possible. Bring all of the data in, shortlist the questions you want answers to, and then start thinking about the data sets and data sources you will need. Data can come in any structure but always keep one question in mind, am I getting insights that can transform my business?
Ajay Anand is the VP of products and marketing at Kyvos Insights. His association with Hadoop goes back to 2007, when he was Director of Product Management at Yahoo and led their initial Hadoop projects and releases, after which he founded Datameer. Anand also held product management and market development roles at SGI and Sun. Heearned an M.B.A. and an M.S. in computer engineering from the University of Texas at Austin, and a BSEE from the Indian Institute of Technology.
Aug2018, Software Magazine
