Reflecting on a year where much has happened across the world of data and analytics, below we make our predictions for key trends to look out for 2017. We’d love to hear your thoughts and comments on them.
With an increasing shortage of data science skills the demand for self-service analytics has never been higher. 2016 saw a shift in organizational mentality to adopt data visualisation technologies to empower their business users to perform self-service analytics to counter-act the skills shortage & improve time to value. This trend will continue to grow throughout 2017 and will in-turn allow data scientists to concentrate on high value, higher complexity work building advanced, predictive and prescriptive models. It will become standardized for business users to create their own reports and analytical views without the need for IT intervention.
With more self-service analytics, the preparation of data will be vital and we’ll therefore see a continuation of the shift towards automated data preparation. Gartner states, “The trend toward ease of use and agility that has disrupted the BI and analytics markets is also occurring for data integration”. Therefore as Gartner predicts, 2017 is likely to see organizations adopt automated data preparation technology, thus eliminating the hours spent building workflows and algorithms by automating these into repeatable workflows that can be used by both technical and non-technical personnel across the business.
Predictive analytics is becoming a reality for many organizations in 2016 - developing algorithms to predict with some probability future events. A safe bet is that as self-service BI and analysis encourages more data-driven business, predictive and prescriptive analytics will become more widespread through 2017. With the ability to continually and automatically process new data to improve the accuracy of predictions and provide better decision options, the focus on the minds of many CIO’s will be to reach the stage of maturity in their data strategy where they can effectively perform prescriptive analytics..
It’s widely reported that 80-90% of business information resides in unstructured formats; it’s been inevitable that the world of analytics would tackle this vast data source. With data now residing in an array of sources that are only growing as a result of the explosion of the Internet of Things (IOT) for instance, technology companies have risen to the challenge and are rapidly developing tools to do this. It will become a necessity for organizations to encompass all aspects of data, structured, unstructured, internal and external or face being left behind by the competition. As well as optimizing the use of existing unstructured data, natural language querying and generation look set to play a big part in 2017, taking self-service analytics to the next level.
The volume of data collected is growing rapidly whether its information being stored from machines, emerging markets or by the internet of things. Yet with this growing volume of data there is still an estimated 95% of information available that isn’t used in business decision making processes, what many describe as “dark data”. As the focus for the future draws increasingly towards AI it is widely agreed that the leading way to make sense of this data is through deep machine learning technology. It is therefore expected to become far more prevalent throughout 2017 allowing organizations to uncover new insights and build upon previous ones.
Another aspect of the machine learning family that we expect will become a key feature throughout next year is cognitive computing technology. The emergence of technologies such as IBM Watson has drawn a lot of attention, allowing humans to engage with powerful AI technologies to achieve analytical insights. It would appear that this is just the beginning as the focus on developing AI, machine learning and cognitive computing only looks set to grow, threatening the existence of traditional BI technologies.
Since the emergence of cloud technology an increasing number of organizations are choosing to migrate their data warehouse into the cloud for the lower cost of ownership, fully managed services, future proofing and increased agility & flexibility that the public cloud provides. The Cloud offers massively parallel processing (MPP), streaming of real-time data and geo-redundant storage with the ability to scale up and down on demand. Easy access to unstructured data storage and analytics within the platforms provides further incentive. The technology has finally caught up with the hype with many now predicting the emergence of the cloud marks the end of the road for the traditional data warehouse.
Embedded analytics boomed in 2016 with a self-service study from Logi Analytics revealing that over 66% of IT teams are using embedded analytics across their organization. Allowing end-users to engage with data-sets and access insights all within their existing work space enhances the insight and value available with increased user adoption. There is therefore little surprise that embedded analytics have grown in popularity so much over the past few years looking set to continue with traditional BI tools expected to adopt embedded capabilities over the course of next year, if they haven’t done so already.
By analysing aspects of customer behaviour businesses can effectively forecast and understand customer needs pivotal for successfully targeting marketing campaigns. Gartner’s recent 2016-17 CMO Spend survey revealed marketing spends have continued to climb and look set to carry on rising throughout 2017. As a result, behavioural and psychometric analytics look set to play a larger role in 2017, being fundamental for businesses in communicating the right messages at the right times to their customers.
Preserving traditional IT practices whilst at the same time adopting innovation is likely to be a key focus throughout the course of next year as businesses look to stay ahead of the competition. The solution, Bi-modal IT, which manages these two separate but equally important modes of IT delivery with one focused on stability and the other agility. With a history of tension between the two, a likely trend throughout next year is for these two modes of IT delivery to look to adapt to work alongside each other in harmony.