Nothing is constant but change.
And perhaps no market changes faster than that of technology. That’s why we at TARGIT are always taking a thoughtful look at how the BI and analytics market has evolved year over year, and predicting where we see it going in the future.
We designed many of the new features and functions of TARGIT Decision Suite 2017
with an eye towards 2017 and beyond. We see the need for faster query power, limitless data capabilities, and an evolution of predictive analytics. These are exciting times for the world of business intelligence and analytics.
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1. The rise of leadership via data
Self-service BI, better and more meaningful data visualizations, and expanded user adoption foster a nimble BI and analytics environment that will define the data-driven leadership culture.
In 2017 more end-users than ever before will be able to wrap their arms around BI thanks to increasingly user-friendly tools that bring data to the masses. A well-rounded BI platform makes it easy to put understandable, actionable data in the hands of employees of every level in every department.
But with just about every analytics vendor talking about how their platform is self-service BI these days, buyers often assume “self-service” equals “user friendly.” That isn’t always the case. A true, modern self-service BI solution is bimodal, focusing on both centralized and de-centralized BI that’s possible for everyone to take advantage of. Self-service will continue to grow as part of the full BI lifecycle of insight discovery, insight testing, and execution that users outside of the IT department can wrap their hands and heads around.
Because of this, more people than ever before will have BI as part of their daily work flow thanks to tools like Storyboards that display KPIs on screens throughout the company and embedded BI that puts important dashboards right inside the applications where employees already work most.
Plus, the year’s improved data visualization tools will make it possible for more people than ever before to create dashboards and analyses. We predict creativity will unfold when departments who are used to static reports and convoluted analyses get their hands dirty with self-service tools. We’re seeing entirely new libraries of charts and graphs and essentially unlimited possibilities of tying data to any customized map or graphic.
This all coincides with the rise of the data storyteller as company leader. BI users can now let their creativity flow when telling the complete story of their data unlike ever before. Drill down capabilities and robust analyses connected to dashboards let users in on every why behind the what, ensuring the full story is told. Those who create these dashboards and visualizations are no longer just data scientists; they’re data storytellers. And those with the data they need at their fingertips are freed to make informed decisions without waiting for guidance from above. Cultivating a data-rich corporate environment empowers every employee to make better-informed choices on their own. Self-service = self-leadership.
Watch how you can better design the entire analytics experience with a better data visualization engine in this free webinar here
2. The time-to-insight window is collapsing
Enormous amounts of data won’t slow down time-to-insight
Just a few years ago, the average company could only dream of access to comprehensive data that was actually instantaneous. At best, this type of technology was limited to the Fortune 100 companies with very deep pockets. But today thanks to the increase in technologies such as in-memory and processes that connect directly to data sources, analytics platforms can process millions of rows of data to be queried in an instant. When companies can integrate and analyze this level of data in seconds, dashboards can reflect data in near real time, giving companies greater insight than ever before on all the data that matters to them.
That’s why the growth of rapid data integration tools will correspond with the growth of rapid analytics tools. Companies will no longer accept slow query performance. In 2016, poor query performance was listed as one of the most often encountered problems with BI projects in companies of every size, according to BARC’s the BI Survey 16
. Slow querying hinders proper adoption of BI solutions across the organization. But with ongoing technology advancements, companies are realizing this isn’t a reality they have to live with anymore. Vendors who can’t provide the speed companies need will be replaced with platforms that can.
This will be something that companies of every size can take advantage of; from small, nimble organizations to intensely structured enterprise groups. In-memory and ad-hoc data discovery fosters an overall environment of exploration and experimentation that traditionally seemed impossible for enterprises that needed the integrity and conformity of the traditional Kimball dimensional modeling technique. Today’s rise in popularity of hybrid solutions means data discovery and other agile deployment options can live alongside Kimball to provide a multi-speed implementation and BI environment that companies can use to take advantage of enormous amounts of data incredibly quickly with the structure they need.
Take the case of Dublin Airport Authority, which tracks massive amounts of data in near-real time as thousands of passengers make their way through the airport’s many check points. This data helps DAA make better staffing decisions of their security personnel and improve overall airport efficiency.
If the waiting time in a particular terminal is longer than airport efficiency benchmarks, managers are alerted so employees can be sent from a slower terminal to open another X-ray line in a busy section immediately. Data is updated every two minutes in the BI system, so administrators and managers are always looking at the most up-to-date information on passenger movement, overall volume, and employee efficiency.
Tracking passenger location throughout the airport also helps the individual airlines make better decisions to help them improve on-time flight metrics. If a passenger has checked in, perhaps online, but hasn’t shown up to the departure gate yet within 10 minutes of a scheduled departure, airline employees can see if that passenger has passed through security. This knowledge tells them instantly if they should push back the flight to accommodate last minute passengers who might just be minutes away or take off as scheduled. This data-driven approach is what has made Dublin Airport the fastest growing major airport in Europe in 2016.
See how TARGIT stacked up against other BI platforms in the BARC BI Survey 16
3. The rise in value of predictive analytics projects
The future is clear with the harnessing of large amounts of actionable data from the Internet of Things
Predictive analytics have largely been a futuristic fantasy for most companies, but 2017 will see a rise in the spread of the types of sensors, algorithms, and technologies that will help companies capture and predict upcoming events, including the harnessing of the Internet of Things (IoT).
IoT has been a sexy topic for years now, but there have been few companies with the resources or purpose to take full advantage of the type of valuable predictive insights that could be derived. In-memory in particular has changed that, as it plays an increasingly large role in capturing the type of data necessary to make informed decisions based off of an impending event, like in the case of the fleet management solution provider, Traffilog, which collects and analyzes thousands of rows of data per second to help employees improve performance and managers make better business decisions.
Sensors installed on every vehicle capture not only driver behavior such as speed, degree of turns, and idle time, but mechanical data such as tire pressure, fuel efficiency, and engine performance. This lets employees know instantly when a vehicle needs maintenance so they can take proactive action and plan accordingly before machinery failure of some kind occurs. It also delivers safety and efficiency metrics so drivers know how they stack up against others drivers and how they can specifically improve their safety ratings with better driver behavior.
4. The stronger need for more powerful data governance
Self-service BI is important, but so is the assurance your data is always correct
As the use of self-service BI continues to grow, companies must place a corresponding focus on data governance. BI users are escaping “Excel hell” with an increasing amount of tools that more easily chart data. But users should be cautious not to mistake any newfound BI freedom for true data integrity. Ease-of-use does not necessarily correlate with data accuracy and consistency. And actually it might be quite the contrary.
More users than ever before are getting their hands dirty with data mash-up, creation of reports, analyses, and dashboards, which could correspond with a rapid dissemination of incorrect data. Just because a spreadsheet can be pulled and mashed up with data that already exists within the data warehouse, does not mean that data is true.
In order to ensure that individual users of BI are free to experiment and explore with data on their own without the IT bottleneck it becomes increasingly important that there are data governance polices in place and that BI and analytics platforms have solid tools in place to help organizations making decisions on the right data at the right time. Today’s modern, bimodal solutions will offer robust data governance that raises red flags before imperfect data makes it out of the rounds of “sandboxing” and into the daily workflow, and assures data only makes its way into the hands of those who are meant to see it.
Read how to improve data quality with sandbox analytics here
5. An increasing reliance on the cloud
Cloud and on-premise data and applications will melt together for data-driven leadership
The cloud will continue to climb the ladder of importance in the BI world in 2017, both as a source of data and a BI delivery platform itself.
An increasing number of BI users need to be able to easily connect to, integrate, mashup, and analyze data from sources outside the existing data warehouse. Not only is it important to be able to pull in data from Excel and CSV files, but data from cloud-based applications such as Google Docs, Google Analytics, Quandl, and an abundance of other diverse cloud- or web-based data sources all play a critical role in a comprehensive view of company KPIs when measuring against the competition, the market, and macro-economic trends.
Cloud-based and hybrid analytics tools introduce a new kind of flexibility compared to the traditional data warehouse structure. This is a major benefit for companies who choose to take advantage, as freedom from the traditional BI infrastructure can drastically reduce BI’s total cost of ownership
Read more about lowering your total cost of BI ownership in this free guide