Nick Golovin is an expert in Big Data integration and the founder and CEO of DataVirtuality. Here he offers his insight on what organizations should know about the future of data integration. Read on…
How has Big Data integration evolved since you started your career?
Since I started my career, data integration has evolved from a seemingly mature discipline relying on classical data warehousing concepts and ETL into a young and fresh field which is facing new challenges and also finding new, great ways of meeting them. The best answers to all of the new questions are yet to be found though – that makes the field of Big data integration a very exciting and rewarding field to work in.
What excites you about the field of Big Data, data analysis, data warehousing, etc. today?
The ability of Big Data and related technologies to change the ways modern businesses work and think, especially when it comes to communicating with customers and serving their needs better.
What are the biggest challenges facing organizations today in managing their data?
A couple of years ago, Gartner published the results of their study containing a very similar question: “When asked about the dimensions of data organizations struggle with most, 49 percent answered variety, while 35 percent answered volume and 16 percent velocity.” (Gartner, 2014, “Survey Analysis: Big Data Adoption in 2014”)
A couple of years passed since then. I have not conducted any comparable study myself, but I think that the respective positions are still the same – however velocity certainly added a few of points due to the increased popularity of IoT use cases which have a lot to do with streaming data.
What have you found are the most common mistakes organizations make in data management?
Some of the mistakes we see on the technology side results from not understanding the strengths and weaknesses of different approaches and technologies. This includes both using classical ETL approaches to handle new Big Data cases, but also using Big Data technologies to solve classical “medium data” problems – both leading to unnecessary IT spend and businesses waiting a long time to get their data.
On the organizational side, the biggest mistake is not getting the right people on board, who are both business oriented and technology savvy and can bridge the traditional chasm between IT and business when it comes to managing and serving the data.
What is your approach to data management? How do you advise your clients to get a handle on their data?
Be smart in applying different technologies and data access methods for different use cases and also for different phases of a BI project. Prototype, iterate then materialize and roll out. If business requirements change – repeat.
What advice do you find yourself repeating to clients over and over on data management?
Start by looking at the business needs and creating business value quickly before addressing the technology challenges of moving or ETL-ing the data.
What challenges do you predict are coming down the road for organizations with data storage? What should they be doing today to prepare for the future?
The organizations should further embrace storing and managing data in the cloud. The proliferation of cloud data management and cloud BI is already quite strong, but according to a recent BARC study (“BI and Data Management in the Cloud: Issues and Trends”, BARC Research Study, January 2017) still only 31 percent of companies in North America and mere 23 percent of European companies have a highly cloud-oriented BI strategy.
What are the most important trends or innovations you’re following in data management today?
The reinvention of the semantic data layer, which was originally championed by companies like Business Objects, Cognos and others, and which was forced into background with the proliferation of self-service BI like Qlik and Tableau. As self-service BI matures and expands on the company-wide level, and at the same time the number of different data sources in the company explodes, the need for a semantic layer becomes more obvious again.
What organizations do you see leveraging data most effectively? What can we learn from them?
We see that digital organizations from verticals like ecommerce, Digital Marketing or other Internet-related businesses tend to use their data most effectively. The key to the effective leveraging of data lies in my opinion in non-IT departments thinking in a data driven way and driving Big Data integration and data management initiatives – this is a trend which needs to be embraced by the more traditional organizations as well.
For more information, watch Syncsort’s video: Simplifying Big Data Integration with a Modern Data Architecture