Analytics and business intelligence (BI) are now considered the most important technology for organisations to differentiate or win, according to Gartner’s 2018 CIO Agenda survey. Twenty-six percent of executives cited BI as crucial, compared to just 10 percent who backed cloud services and 6 percent for mobility.
With an 8.2 percent project compound annual growth rate (CAGR), Gartner projects the BI market should reach an estimated value of $27.6 billion by 2021. Unpacking key trends in BI and analytics is likely key to surviving the inevitable wave of disruption that’s just around the corner.
4 Key Trends in Operational Intelligence
1. Blur of Machine Learning and BI
There’s a new BI professional in town — the citizen data scientist. Despite the 2012 Harvard Business Review headline that declared the data scientist was the “sexiest job of the 21st century,” there’s too few individuals who fit the bill. Data scientists are one of the most severe skills shortages of 2018.
Skills shortages have contributed to increased blur between modern BI platforms and machine learning platforms. The individual who uses these tools relies on advanced analytics and predictive models but may lack a background or job in AI, machine learning or stats.
2. Cloud Analytics Reaches Tipping Point
2018 has been declared the “tipping point” for cloud analytics by Digitalist Mag, as enterprises increasingly ditch old processes for end-to-end cloud analytics models. The payoffs are a new view of the customer and operations via real-time insights.
3. Context-Rich Insights
In the past, data-driven decisions meant leaders reviewed static dashboards. BI apps were run on-premises with on-site expert users and custom models for each of the firm’s data silos.
The current wave of BI disruption is transforming the way users connect with insights. Insights find the users at the right time, with analytics that adapt to the user, conversation and process.
4. Discover, Self-Service Top Growth Predictions
Per Gartner analysts, some of the fastest-growing types of BI apps based on five-year CAGR projections include:
Augmented data discovery
Search-based data discovery
Self-service data preparation
5 Ways to Prepare for the Future of Business Intelligence
Organizations overwhelmingly recognize that BI and analytics software can present a significant competitive advantage. Firms with the foresight to transition from traditional BI platforms and data science tools and adopt today’s industry trends could gain the ability to recognize opportunities, transform and respond faster than their competitors.
1. Prepare to Transform Your Organization
Trends in operational intelligence are removing layers of separation between knowledge workers and data. Instead of having to wait for data scientists or BI experts on staff to deliver insights, users can take advantage of increasingly self-service tools to discover insights and predictions on their own.
Preparing to transform your organization to take advantage of the new citizen data scientist probably isn’t optional either. Mary K. Pratt writes in CIO, “The speed at which users need access to data and insights … has increased dramatically in recent years.”
Pragmatically speaking, this means that business leaders should prepare to take more ownership than ever before on analytics software selection initiatives. Partnering with IT to make sure data assets are accessible and useable is crucial, as is investing in training to make sure your staff can make the most of self-serve new BI tools.
2. Adopt More Sophisticated Analytics
Virtually all organizations have analytics and are seeing value from it. Recent HBR data revealed 97 percent of surveyed enterprises have invested in analytics, and 73 percent have realized returns. However, the firms that win are likely to be the ones that push their data analytics culture to the next level and “move ahead” with more sophisticated data analysis:
Log and transactional intelligence
Social and graph-based insights
3. Improve Role-Based Insights
While many organizations are still working towards transforming into a truly analytics-driven culture, self-serve BI tools offer enhanced, role-based intelligence for employees at all levels of operations. Customers and employees can benefit significantly from decisions that are made based on real-time insight into cloud data sources.
With cloud apps, leaders have the ability to take advantage of predefined user roles or design streams of context-based insights. Use cases for role-based insights for frontline employees include:
1. Providing CSRs with smart customer service recommendations based on social, location-based and other behavioral intelligence on customers
2. Building document-recommendation and information-finding features into team collaboration tools for knowledge workers
3. Enhancing the depth of behavioral insight on customers and competitors available to the sales team
4. Add Intelligent Processes
Embedding machine learning and analytics software into your business processes, with cloud apps, can enable organizations to achieve efficiency and agility. Examples of intelligent processes can include the integration of RFID tags, IoT data or other real-time analyses from fast-moving data streams available to business users.
5. Create a Business Intelligence Infrastructure
Embedding analytics into your processes and culture requires the right infrastructure to support self-service business intelligence and a fleet of citizen data scientists. Transitioning to the cloud enables end-to-end analytics, role-based intelligence, intelligent processes and other key aspects of the current BI disruption wave.
Moving your analytics infrastructure to the cloud can also support your ability to find and prepare the right data sets, without running into silos.
Conclusion: Riding the Next Wave of BI Disruption
Change is inevitable in the realm of business intelligence, and CIOs overwhelmingly realize that data analytics is key to competitive advantage. Preparing for the upcoming wave of disruption requires leaders to take action and adjust their infrastructure, technologies and processes to create a workforce of citizen data scientists.