Top 10 data management trends for 2016
Manish Sood gives his predictions for how cloud and data solutions will evolve over the year.
1. Public cloud platforms will continue to gain momentum. With the unprecedented success of Amazon Web Services (AWS), Google Cloud and others, it's clear that a growing number of companies have got past the phobia of storing their data and having data-driven applications built on public cloud platforms. While some industries remained cautious, the cost and elasticity benefits cannot be ignored. AWS's recent announcement of QuickSight is yet another example set to potentially disrupt the multi-billion dollar business intelligence and analytics market.
2. Big data and Internet of Things will remain too big to ignore. While the term big data has been overused, the reality is that not many enterprises in B2B have taken the plunge. Talks at big data conferences still discuss fundamental concepts, and industries such as pharma, which previously never really considered their data 'big' are beginning to realise that they need to plan for the future. Though size is not what matters, an increase in variety and sources of data will provide more relevant insights and better outcomes.
3. Hadoop will get thrown for a loop. It is hard to believe that Apache's open-source software framework Hadoop is over 10 years old. While interest remains strong and usage is maturing, there are new options that either complement, or provide an alternative to, Hadoop to handle big data, such as Apache Spark and Apache Drill. Further options will be available this year.
4. NoSQL and Graphs will become 'legit'. The Gartner Magic Quadrant for Operational databases features a significant number of NoSQL [Not only Structured Query Language] companies, including DataStax with the Cassandra Database in the leadership quadrant. NoSQL and Graphs concepts are permeating the enterprise landscape, where the schema-on-read, high scalability and real-world representation of relationships are prized. Google, for example, uses a combination of Bigtable and its Knowledge Graph. In 2016 NoSQL and Graphs will take a leading position in the marketplace.
5. Operational and analytical will get hitched. The notion of operational and analytical processing goes beyond a new wave of databases that can handle diverse workloads. Data-driven applications such as LinkedIn and Facebook have led the way for years in delivering a single, unified contextual experience that combines both analytical-relevant insight with operational application execution. There will be an accelerated adoption of data-driven applications to solve the most pressing challenges.
6. Predictive analytics will no longer stand alone. While there is a plethora of tools for big data analysis catering to a new generation of data scientists, it is becoming increasingly evident that enterprises want macro insights, but they also want them to be more relevant to their day-to-day business users. For that to become a reality, enterprises will no longer focus just on standalone insights and recommended actions, but on those that form a closed loop of actions to outcomes, built on a foundation of reliable data.
7. Mobile UI will emerge as the new normal. With the spread of mobile devices and the new normal of accessing applications on the go, day and night, mobile user interface (UI) for enterprise applications will become a pre-requisite, not just a luxury.
8. Data acquisition and augmentation will be simplified. Despite the advances in data integration and the blurring of the lines between batch (ETL) and real-time (EAI) integration, the process towards managing and blending third-party or externally public datasets continues to be a burden for IT, and a business cost, both financially and through missed opportunities. Data as a Service (DaaS) built in to data-driven applications will change the game dramatically, not just for acquiring external data, but for sharing data internally and providing the opportunity to monetise data through outbound licensing.
9. Democratisation of governance will drive collaboration. Data governance is no longer just the domain of IT and compliance teams. Today self-service and collaborative data management dictates that everyone has a shared responsibility for ensuring the quality and security of information. Business users will get involved with the quality and governance of data, as partners with IT, by adding value through social collaboration on data sets through the course of their day-to-day activities.
10. Master Data Management (MDM) will become common. Historically, only large companies with big IT teams and budgets for hardware, software and multi-year implementation projects have been able to afford MDM. Cloud MDM, a more recent phenomenon, has made it more widely accessible, but it is still niche and an IT function. A new breed of data-driven applications will come with MDM built in. As a consequence of delivering both operational and analytical functionality, the reliable data foundation of each application is powered by an MDM engine. By focusing on the business value that needs to be delivered through these apps, data is continuously cleansed and blended together so relevant insights and recommended actions can be obtained. These capabilities, delivered through public cloud infrastructures such as AWS, will make it affordable for companies of all sizes in any industry for any use.
About the author:
Manish Sood is the CEO of Reltio, creator of data-driven applications. Prior to founding Reltio, he led product strategy and management for the Master Data Management (MDM) platform at Informatica and Siperian. During his career, he has been the architect of some of the largest and most widely-used data management solutions used by Fortune 100 companies today.
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