Big Data is Becoming Less About Size and More About Combinations
Leveraging all data is emerging as a very significant trend impacting the value organizations can drive from analytics is their ability to consume data.
The next wave will be about the ability to quickly combine big data with small data to serve specific higher value use cases. And that will become easier to do as the focus shifts to data semantics and catalogs, and to standardizing data models more easily with external data sources. With our recent acquisition of Podium Data, Qlik is leading the way in enabling the customers to unlock this opportunity. Joining in with the success story of Qlik, Armstrong Mejilla, Presales Team Lead, Qlik in exclusive talks with Niloy Banerjee | Consultant Editor | BIS Infotech taps the ‘Big’ trends of ‘Data’ and cites on how Qlik’s leading approach is enabling Data as a valuable tool for businesses. Edited nub.
- How is Big Data evolving in 2018, the maturity of the market and the potential?
The Big Data and Analytics industry are presently at a very exciting stage. IDC estimates that the global Big Data and Business Analytics revenues in 2017 exceeded $150 billion, marking a year-on-year growth of around 12.4%. It also projects the market to continue on its double-digit growth trajectory through to 2020, driving revenues in excess of $210 billion. These are encouraging signs that the global Big Data market will continue to grow.
Leveraging all data is emerging as a very significant trend impacting the value organizations can drive from analytics is their ability to consume data. We see an increasing need to easily combine different data sources and data sets because this is where the real value lies. This will drive faster time-to-value and result in better data quality.
It must be acknowledged that the first wave of big data analytics has met with limited success and data warehouses often failed to fully deliver on their promise. Throwing data into a lake or warehouse, or just hoarding data for no clear purpose has proved inefficient in most cases. Gartner validates this by saying that through 2018, 90% of deployed data lakes will be deemed useless as they are overwhelmed with information assets captured for uncertain use cases.
The next wave will be about the ability to quickly combine big data with small data to serve specific higher value use cases. And that will become easier to do as the focus shifts to data semantics and catalogs, and to standardizing data models more easily with external data sources. With our recent acquisition of Podium Data, Qlik is leading the way in enabling our customers to unlock this opportunity.
- Standing in 2018, has big data overcome the allegations of privacy and unregulated data mining?
Whether Big Data has overcome these allegations or not is a matter of opinion. The bottom line is that it’s about use and misuse. While it is obvious that our privacy is threatened by the unregulated use of personal data, it would be irresponsible to say that data mining is all-harmful. It has been proved that applying data analytics on top of large datasets can augment the creation of untapped valuable knowledge. New data protection laws like the EU General Data Protection Regulation (GDPR) heralds a new era of privacy regulation. With more stringent rules and significant penalties, GDPR compels organizations to ascertain beforehand how they are using data, how long they are keeping it for and makes the case to use trusted vendors. Qlik is a GDPR-compliant vendor with an ongoing commitment to protecting our data and the data of our customers and partners.
- How can one secure its Big Data off or on-premise?
Characteristic of a modern data analytics platform is the ability to balance flexibility and security. The platform should handle authentication using digital certificates and Transport Layer Security (TLS) to encrypt network traffic and in turn, allow for Single Sign-On with identity providers. It should also provide an authorization framework that is scalable, secure and governable.
This scenario gets more complicated with companies looking to combine analysis with data on-premise and in the cloud. Qlik’s Multi-Cloud offering is well-positioned to address this growing need with its container-based architecture that easily and economically scales across on-premise and multiple cloud environments.
- How majorly has Big Data Market impacted the Analytics Market?
The definition of big data is evolving. For a number of years, it’s always been about volume, velocity, variety, and veracity. A lot of times that applies to data regardless of size, quantity, and shape. However, what we need to do is to assess the latency and the relevancy of data to the needs of an organization. You may need to consume large volumes and varieties of data to satisfy that. That data may be at different velocities. This means the notion of big data is disappearing and, in the future, we will just call it data.
Gartner found that successful implementations typically focused on combining and then analyzing different kinds of data as opposed to exploring one giant data source. The need to relate together different kinds of data – internal and external – will provide fresh context and reveal new insights. Qlik ensures that everyone can get exactly what they need and when they need it, to support better data-driven decisions, a trend which is likely to continue growing as the availability of external and cloud-based data increases.
- Lastly, how your company is determining or say linking the future of Big Data in terms of technology, market adaption, and revenue.
Big Data is becoming less about the size and more about combinations. With more fragmentation of data, and the majority of it created externally and in the cloud, looking at singular data sets without context will diminish in value.
Many organizations are figuring out how to see real value from their “big data” efforts. The best way to analyze big data is to leave it where it resides and create access without the need to extract every piece of information. Qlik is creating its Associative Big Data Index for exactly this need, so organizations will be able to have users freely explore and search big data within sources such as data lakes and Hadoop without limits. It will make big data accessible and scalable across the entire organization while adhering to corporate governance and compliance policies.
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