Ever since data warehousing began in the early 90’s the IT sector has been undergoing rapid change. The emergence of Business Intelligence, or BI, has helped organizations leverage data to make crucial strategic decisions, improve operational efficiency and business productivity.
With the advent of self-service technologies like Tableau, QlikView, and Domo, more organizations are successfully deploying BI. This is empowering business users to quickly analyze the data and generate deeper insights independently.
Despite advances in technology, firms that can access and leverage their data, now have a new problem on their hands, how to be successful with it? To better understand how companies can extract value from their data using self-service platforms I connected with Uday Hegde, co-founder, and CEO of USEReady, a business performance consultancy that helps organizations use data to improve their businesses.
Are there any industries catching on to the importance of Self-Service BI faster than others?
Hegde: Banking, high-technology, insurance, retailers and healthcare firms are adopting Self-Service BI innovations at rapid rates.
As these verticals have to deal with the increased impact of 4Vs of data (volume, variety, velocity & veracity), they need an advanced BI solution to maintain a competitive advantage. Technological advancements are further driving BI adoption among these vertical, such as cloud computing that has only recently become available.
How has BI benefited large-scale organizations?
Hegde: For the last few decades, IT departments have been the gatekeepers of valuable data. With the rise of self-services tools employees now have access to data that was previously inaccessible. This enables users to perform independent analytics which accelerates the process of decision making, highlighting the importance of democratizing the use and management of data.
To that end, large-scale organizations are creating roles dedicated to the cause, often calling them Chief Data Officers or Chief Analytics Officers, in order to harness their proprietary data and be self-reliant with business intelligence.
How does data visualization improve Business Performance?
Hegde: Data Visualization enables business owners to present the health of their business in a meaningful way with new insights. Meticulously visualized data enables business owners to ask critical questions and make more informed decisions about leveraging limited resources, whether they be people, processes, or technology.
Traditionally, the BI sector focused on one of the three branches of analytics called “Descriptive Analytics”. Descriptive analytics simply focused on reporting historical data in a meaningful manner presented as graphs and charts. Users today need more than just descriptive; BI and Visualization are moving towards predictive and prescriptive analytics which give businesses the capability to foresee and predict the future and take proactive measures today.
We once worked with a public awareness campaign agency that spends billions of dollars every year. We helped them understand national, regional media outlet performance and campaign efficacy by using advanced data visualization techniques. With the help of interactive dashboards, they were able to make spending decisions more efficiently than before.
Big Data is the buzz word of the year, but what kinds of data and intelligence are going to be the most useful in the coming year?
Hegde: While Big Data isn’t a new concept we are still in the early stages when it comes to utilizing the information, we collect. Early adopters have used big data platforms to move away from expensive legacy systems like storage tapes and mainframes.
In order to help organizations leverage big data, we expect that a confluence of Data, Cloud technology, and the Internet of Things (IoT) must occur. This will help organizations develop technology solutions that can improve our lives in significant ways.
How important is information security for Business Intelligence and Data?
Hegde: Imagine that Data and Security are at two ends of the same stick. You can’t lift one without lifting the other. Many organizations are realizing that democratizing data creates unanticipated security challenges. Knowing how data is being accessed by all the stakeholders inside and outside an organization is a key consideration for every Chief Data Officer. It is imperative that large-scale organizations have to take a fresh look at their data infrastructure and make appropriate decisions.
At a market leading insurance organization, they have adopted what we call a “Hybrid Networked BI” approach. For BI, they are allowing individual departments to use a self-service tool of their choice such as Tableau, Qlik, Microsoft Power BI etc. For data, they have built a networked data fabric that supports data needs of the BI tools. Networked BI is supported by a single vendor data platform as a virtualized data lake. This enables them to transition from the world of disparate data sources to single data source to ensure they have appropriate data governance in place.
How can businesses work to make their BI more scalable?
Hegde: BI data sets can be scalable when they are democratized, and with advancements in Cloud computing, scaling BI needs is no longer a challenge. A decade ago, large banks could not scale their data layer beyond a certain geography. For e.g. many Swiss banks operating globally had a need to analyze Risk centrally, but sensitive customer data had to remain regional due to secrecy laws. This implementation would be cost prohibitive a decade ago whereas now it is no longer a challenge.
At USEReady, we have worked with large global banks that are solving this problem via Networked BI approach. Essentially this allows regional data to reside in respective geographies and Risk measures are viewed globally in a central location. This is an example of scalable BI in a large organization.
What changes or innovation do you think will occur in the BI sector in the near future?
Hegde: Cloud-based Data Warehouse (CDW) will gain popularity among businesses so that they are able to support scaling BI needs. We also think that there will be a shift towards networked BI with an API-centric approach to data integration to make the data more usable. Governed data preparation at scale for BI will change the way businesses engage with their data. Also, analytical apps driven by a virtual data fabric for department BI will also become more prevalent.