1. The Continued Importance of Embedded Business Intelligence
Embedded analytics refers to the integration of analytics into other business applications to provide the users of those applications with self-service, data visualization, BI reporting, and dashboards for in-context analysis and immediate action. As data continues to grow in abundance and importance, so too has the interest and demand for effective ways to utilize data in the context of business end user’s day to day workflows. Software vendors have looked towards 3rd party embedded analytics vendors to fill the need.
The effectiveness of data is often tied to a users ability have it delivered to the right place, at the right time, in the right format. You could have all the data in the world, but if you have no meaningful way to organize, distribute, and analyze that data it’s essentially meaningless. User-based self-service platforms found outside of business applications rely on either additional secondary actions by end users to load and process data, or a fully governed system which relies on IT intervention to actively manage BI reporting capabilities. Moreover, these systems often have a lower barrier to entry but become significantly costly at scale. Because of this, more and more users and IT groups are demanding analytics be made available directly in the applications they already use.
As the needs of business end users grow, embedded business intelligence will continue to grow in importance. The best in class vendors in the embedded space, like JReport, will continue to focus on enhancing both end user and developer experience. For users, this means more ways to visualize and interact with data in more meaningful ways. For developers, this means ever easier ways to embed, more integration options for deeper integrations which provide more options to enhance existing application workflow and built-in administrative tools to manage users with ease.
2. The Proliferation of Chief Data Officers
Today analytics and data play an ever-increasing role in business, and that fact is not lost on executives looking to more effectively and efficiently run their businesses. Over the years, the responsibility of deriving meaningful insights using BI has shifted away from general IT and more towards dedicated BI departments. But in many businesses, there remains an inescapable gap between the organization, consolidation, and governance of data still controlled and managed by IT and the driving speed of actionable insights businesses demand.
The increased strategic importance of BI has thereby driven the creation of a wholly new C-level executive position. The Chief Data Officer (CDO) also known as the Chief Analytics Officer (CAO) position has been created in many enterprise organizations to help better manage the increased usage of data analysis and bring a new level of accountability and insight to every business division.
In most cases, the structure, governance, and security of data still falls into the hands of the Chief Information Officer (CIO) or the Chief Technology Officer (CTO), but the CDO now has the responsibility to manage the derivation of actionable insights for use in process improvements, decision making, and general business reporting.
This elevated focus and investment in business intelligence will continue to grow as the ever increasingly data-driven world brings to light new possibilities for growth. And enterprise organizations’ ability to effectively manage and proactively understand the past, current, and future nature of their business will continue to be a point of strategic importance. The impact of which will show in how organizations strategically organize their resources to be most effective.
3. Data Quality Management (DQM)
The increasing abundance and availability of data has improved businesses ability to effectively make decisions and operate. But data-driven decisions are only as good as the quality of the data behind them. Inaccurate data can lead to treacherous waters when important decisions are made with them. One of the biggest problems enterprise organizations now face is how to ensure their data is as accurate as possible.
The answer to this has largely been an increase in focus on measuring and maintaining the accuracy of data within the enterprise and an increase in the level of accountability to which those in charge of internal data resources are held. This can be done by better defining organization-wide data and data quality management strategies, defining what quality data means for your enterprise and putting a process into place to ensure the right people and metrics are involved and set throughout the data processing and analysis processes. Just as with CDOs, as the role of BI reporting continues to increase within the core business operations of enterprise organizations in 2018, businesses will continue to react with organization-wide structural changes meant to refocus on data quality.
4. Multi-Cloud Strategies and BI Reporting
Cloud has quickly become a staple strategy for many enterprise organizations both in the deployment of their internal applications as well as the products they sell. The same is true when it comes to BI. More and more organizations will be leveraging cloud for BI reporting in 2018. And it makes sense that having all of your data resources, processing and compute, and data storage in the cloud has many benefits. Among others, meeting peak load times can be more easily accomplished without the excessive overhead of physically purchasing and maintaining high compute hardware.
But there are many in the IT world who aren’t 100% sold on single vendor cloud strategies because the idea of putting all your eggs in one basket has never really been an accepted ideology in IT, even if that basket includes multiple data centers. And it’s not an unfounded concern, as recently as last year there was a major crash from a leading vendor in cloud solutions1. So what’s an organization to do?
Well, many organizations are beginning to look at implementing multi-cloud strategies, wherein, they leverage multiple cloud providers to minimize the risk associated with a major failure, while still reaping all of the cost and management benefits cloud reporting presents. The rest of the year we’ll likely see many companies who have been cautious moving to the cloud, starting to implement this type of a strategy for BI reporting.
But, that’s not to say that multi-cloud doesn’t have its drawbacks. Implementation and training costs associated with having to potentially have multiple teams trained on different platforms or having a single team trained on multiple platforms can be a real concern. And the savings you receive with economies of scale using a single source vendor are also partially erased when moving to multiple vendors. Either way, we expect to see the adoption rate of multi-cloud for BI reporting increase substantially this year.
5. Predictive Analytics, AI, and Natural Language Processing
Up to this point, we’ve talked about BI reporting technologies and capabilities whose value is widely understood and accepted, and the use of which has become relatively ubiquitous across the enterprise. Enhancements to these technologies will continue to be important for years to come as the need for analytics continues to evolve. But as we look forward to the future, we’ll be increasingly, well, looking forward to the future.
The emergence of machine learning and predictive analytics has given businesses a glimpse into the future of data, and natural language processing has given us a glimpse into how our interactions with data will change in the future. Though how businesses can best use these technologies has yet to be fully realized, it is indisputable that they will play a major role in the future. Let’s take a look at how:
Predictive analytics has been around for a long time. To some degree regression techniques and data modeling have been used by businesses to help forecast future data based on past data for years. But the level of reliability in future estimates has always been reliant upon and hindered by the accuracy and depth of the data used. And the use cases for this type of analysis have been widespread but not necessarily widely adopted by all industries and businesses.
The nature of predictive analytics has applied itself more readily to certain scenarios and industries, ones which produce many data points used for comparison and have been particularly useful in ones where there is finite supply, high sunk costs, and elastic demand. Industries such as the hotel and airline industries could be considered early adopters of these types of analysis because the business value was so immediately understandable and the need so great to predict customer’s willingness to pay at different times and prices. But the usage of predictive analysis has continued to grow and expand as systems have become easier to use and more economical. This year we expect the adoption rate of predictive technologies to continue upwards.
Prescriptive Analytics and AI
Predicting what might happen is one thing, but prescribing action for the purposes of reaching an intended goal, or put in another way, stating what can happen and steps how to get there is a trend that will continue to grow this year and into the foreseeable future. In recent years, prescriptive analytics, AI, and machine learning have gone from being futuristic concepts used by companies to show off their technological prowess to being commercially viable products on the cutting edge of what is possible in business intelligence. And it’s easy to see why. The ability to understand what might affect future decisions can greatly improve decision-making in a very heuristic way, and as the depth and breadth of data collected by enterprise applications expands, we expect the deep learning and advanced pattern recognition these systems are capable of to be even more important within the realm of analytics. As the use cases for prescriptive analytics become designed, developed, and proven we expect to see more widespread adoption of the technological features within day-to-day business operations.
Natural Language Processing (NLP)
The emergence of virtual assistant applications like Alexa, Siri, and Cortana has signaled an elemental shift in how we communicate with and use computing and technology. Though the underlying affects on society this shift represents are still yet to be understood, they will for all practical purposes change how we interact with data in a fundamental way.
The accrual of knowledge and more specifically data in enterprise business systems has largely been limited to the physical data collected and generated through the interactions, changes, inputs and imports users make within a given system. With the rise of NLP, the collection of data has now greatly expanded to include many new pathways. And with the natural language and speech recognition capabilities of NLP systems improving in quality daily, the ability to collect coherent, quality data from speech will have a wide-ranging impact on the ways in which we look at actionable business intelligence and the relationships between human behavior and business efficiency.
But it is not just the data that will change. The way we interact with data will be greatly expanded with NLP technologies, and the usability of even advanced BI reporting tools will increase drastically with this emerging technology. The virtual barriers to entry represented by complex analytical systems and data structures will be replaced with simple oral communication. And when paired with machine learning and AI, these capabilities will represent a shift in how individuals will understand and interact with business intelligence.