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December 22, 2016updated 13 Jan 2017 11:39am

2017 predictions: Big data and its coming of age

Kill some time as you escape from the family and find out what you should be on the look out for in 2017.

By James Nunns

It’s that time of year again, the streets are full of festive cheer, amusing jumpers are being worn, and belts are being loosened as mince pies, mulled wine and cheese threaten to force you into a larger size of clothing.

That’s right it’s Christmas and that means the New Year is just around the corner.

As traditional as a post Christmas dinner lunch snooze, predictions for what to look out for next year are here to give you something to look forward to, or live in fear of.

Big data may have had a quiet year but it’s definitely not going away, so here’s what to look out for in 2017.

 

Aaron Auld, CEO Exasol

Self-service BI will go mainstream: Self-service tools are gaining ground in the enterprise and startup alike. As data analytics integrates itself further into the core of the business, there will be a shift towards the business diving into data analytics with databases, visualization tools such as Tableau and data-prep tools such as Alteryx. Business users have begun to use the tools that take the time and complexity out of data analytics; this is very important as enterprise deals with different data types and formats

Machine learning and AI becomes embedded within the database to drive predictive analytics: This will enable more advanced algorithms and better actionable insights. It will also see solution providers develop even more bespoke services that respond to the needs of certain issues, for example, predictive analytics for replenishment in retail.

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John Schroeder, CEO and founder, MapR
Big Data for Governance or Competitive Advantage

“In 2017, the governance vs. data value tug of war will be front and center.

“Enterprises have a wealth of information about their customers and partners. Leaders are transforming their companies from industry sector leaders to data driven companies.

“Organisations are now facing an escalating tug of war between governance required for compliance, and the use of data to provide business value and implement security to avoid damaging data leaks and breeches. Financial services and heath care are the most obvious industries with customers counting in the millions with heavy governance requirements.

“Leading organisations will manage their data between regulated and non-regulated use cases. Regulated use cases data require governance; data quality and lineage so a regulatory body can report and track data through all transformations to originating source. This is mandatory and necessary but limiting for non-regulatory use cases like customer 360 or offer serving where higher cardinality, real-time and a mix of structured and unstructured yields more effective results.”

data-lake

Data Agility Separates Winners and Losers

“Software development has become agile where dev ops provides continuous delivery. In 2017, processing and analytic models evolve to provide a similar level of agility as organisations realise data agility, the ability to understand data in context and take business action, is the source of competitive advantage not simply have a large data lake.

“The emergence of agile processing models will enable the same instance of data to support batch analytics, interactive analytics, global messaging, database and file-based models. More agile analytic models are also enabled when a single instance of data can support a broader set of tools. The end result is an agile development and application platform that supports the broadest range of processing and analytic models.”

 

Tim Seears, VP Emerging Technology at Think Big, a Teradata Company

“Data analysis has become an increasingly vital component of business and I fully expect its growth trajectory to continue through 2017. It will be the year of predictive analysis, which will help businesses to unlock new insights and predictions, with robotic and automated adoption increasingly replacing traditional manual processes.

“This will not only be a huge cost saver for business, but it will also expedite them through the boring’ admin stage so that they can move on to applying deep learning properly and getting maximum value from it. I expect to see more companies beginning to realise value from their big data investments and applying that knowledge in new ways, while data analytics will become increasingly prevalent in industries such as agriculture and healthcare.”

Apache Spark wars on the next page

Basho EMEA Solutions Architect, Stephen Etheridge:
NoSQL & time series

“In 2017, everything in the world of NoSQL is going to be driven by time series data. In the coming year we’re going to see a lot of companies claiming their database is suited for time series, even if it isn’t, and it’s more of a forced fit.

“The reason for this is that everyone is seeing the opportunities that IoT is offering up, and properly storing and managing time series data at scale is key to capitalising on these opportunities.

“It could be argued that a gap in the market relates to time series and Hadoop. Lots of people still use Hadoop, and there are lots of things you can slot on top of it, however nothing strictly built on top of Hadoop could be considered a time series database.”

PredictionsApache Cassandra

“The Apache Software Foundation, home to the Cassandra Project, has cut its ties to DataStax, the primary contributor to Cassandra in recent years. Cassandra’s use in 2017 looks quite foggy.

“People may move away because there’s no guarantee of stability or consistency. Many will say this will be an orderly changing of the guard, however it’s still going to be an uncertain and confusing time for developers. Big companies certainly won’t be looking to adopt the technology or sink any real investment in. It’s one of those elephants in the room but definitely a topic which will be debated more in the coming year.”

 

Spark vs Flink

“The ‘Spark wars’ will continue. I would expect this to be a two horse race between Flink and Spark. It doesn’t look as though another contender will emerge. Some markets are still using Storm however the industry as a whole sees this as being a few years behind.”

Database innovation will be a big part of 2017. Find out more on the next page.

 

BashoCEO, Adam Wray

“In 2017, NoSQL’s coming of age will be marked by a shift to workload-focused data strategies, meaning executives will answer questions about their business processes by examining the data workloads, use cases and end results they’re looking for.

“This mindset is in contrast to prior years when many decisions were driven from the bottom up by a technology-first approach, where executives would initiate projects by asking what types of tools best serve their purposes. This shift has been instigated by data technology, such as NoSQL databases, becoming increasingly accessible.

“In 2017, organizations will stop letting data lakes be their proverbial ball and chain. Centralized data stores still have a place in initiatives of the future: How else can you compare current data with historical data to identify trends and patterns?

“Yet, relying solely on a centralized data strategy will ensure data weighs you down. Rather than a data lake-focused approach, organizations will begin to shift the bulk of their investments to implementing solutions that enable data to be utilized where it’s generated and where business process occur – at the edge.

“In years to come, this shift will be understood as especially prescient, now that edge analytics and distributed strategies are becoming increasingly important parts of deriving value from data.”

 

Vinay Joosery, CEO Severalnines
Database innovation, what will we see in 2017?

“One of the more salient points businesses should consider as we’re moving to an increasingly proliferated cloud deployment market is driven by ‘Data Gravity’. Apps and services do not run very well when data is separated from them, they require proximity to data.

“The large cloud players like AWS, Azure and IBM are wise to this and are expanding their capabilities to accommodate this concerted effort towards cloud with DBaaS offerings and we’ll see a lot more of this movement throughout 2017.

“This does bring in another issue which has had some publicity and that’s the problem of cloud lock-in. Some believe vendor lock-in does not exist anymore thanks to subscription-based business models. Having all your data reside in one cloud, accompanied by the notion of ‘Data Gravity’, means your applications would also tend to  gravitate to that cloud.

“Eventually, you become dependent on the tools provided by the given cloud vendor. This may result, as businesses become aware of this, in the diversifying of their infrastructure, so they don’t put all their eggs in one basket.”

bigdreams

Hortonworks Chief Technology Officer, Scott Gnau
Real Time Machine Learning and Analytics at the Edge

“In 2017, ‘centralized-only’ monolithic software and silos of data disappear from the enterprise. Smart devices will collaborate and analyze what one another is saying. Real time machine-learning algorithms within modern distributed data applications will come into play – algorithms that are able to adjudicate ‘peer-to-peer’ decisions in real time.

“Data has gravity; it’s still expensive to move versus store in relative terms. This will spur the notion of processing analytics out at the edge, where the data was born and exists, and in real-time (versus moving everything into the cloud or back to a central location).”

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