The Current State of Analytics & Business Intelligence

According to IDC’s State of Data Science and Analytics report, data is becoming increasingly critical to success in the digital economy. As much as 80% of organizations rely on data for multiple processes—from product management and fraud detection to finance, HR, and manufacturing. However, while most businesses depend on data, the reality is that the majority of organizations aren’t realizing its full potential.

Another report, Microstrategy’s 2020 Global State of Enterprise Analytics, revealed that the move toward self-service analytics that dominated the 2010s hasn’t delivered on its promise to arm businesses with the intelligence they need to make informed decisions. In fact, the report’s findings revealed the opposite. Many organizations describe themselves as “data-driven” yet rely on low-tech solutions like spreadsheets, or worse, gut feelings, to inform business decisions.

In this article, we examine the current state of augmented analytics and what’s next.

How Augmented Analytics Helps Businesses Make Data Driven Decisions

Data dashboards and visualizations have become staples of the modern workplace, allowing users to use pre-made reports and filters to track performance metrics on-demand. Despite the perceived user-friendliness of today’s analytics tools, organizations struggle to prepare and comprehend their data—much less put it to good use.

An estimated 85% of Big Data initiatives turn out to be big failures. Projects can fail for all sorts of reasons: poor data integration, a lack of resources, the skills gap, and departmental barriers, just to name a few. What’s really troubling is, those numbers haven’t changed much since 2015.

Augmented analytics stands to change the state of analytics dramatically, using self-service, automated solutions to help organizations empower their workers with on-demand access to accurate information. This wave in analytics brings artificial intelligence (AI), natural language processing (NLP), machine learning (ML), text mining, and automated data processing into business intelligence (BI) systems.

Platforms automatically gather the right data, detect patterns, model data for analysis, and serve up coherent insights to business users. Non-technical users can access real-time insights without relying on IT, data scientists, or analysts to pull basic reports anytime a decision must be made.

According to the Microstrategy survey, the current state of business intelligence is such that 80% of managers have access to analytics platforms. In contrast, only half of all front-line employees have access to those tools.

Much of the buzz surrounding augmented analytics is that it democratizes analytics, making it possible for everyone—from customer service and marketing to the C suite and the IT team—to use data to improve their performance.

Increased Demand For Analytical Talent

IDC found that data complexity, diversity, and volume were the top challenges for data workers that reported spending more time on analytics than data science or development. What’s more, 88% of those data workers still use spreadsheets to manage data activities, a manual process that, on average, eats up about 60% of working hours.

The Microstrategy report also found that of the employees lacking the necessary data skills to make a business decision, 44% ask for help from IT, 35% seek out a business analyst, and 11% go with their gut. Only 4% actually do their own research.

The skills gap issue has given rise to the idea of the “citizen data scientist,” which isn’t an official role you might find on a site like Indeed or LinkedIn. Rather, the term describes business users outside of the data science field with the skills and capabilities to extract actionable insights from complex datasets. According to an article in CIO, emerging data science tools are democratizing the “low-end” segment of data science tasks.

The idea is, augmented analytics platforms allow business users more advanced capabilities than what you find in most self-service analytics platforms. Users gain the ability to enter a query and capture valuable insights while data scientists can focus on more complex tasks.

Bernard Marr, an expert on Big Data, points toward Sears to clarify that point. He stated that the department store recognized a need to improve its customer segmentation capabilities. The brand needed people that were more tech-savvy than the average Excel user but didn’t need people with advanced data science skills. To respond to this need, Sears reskilled existing staff to become Big Data analysts, allowing the brand to gain a better understanding of its customers.

The point is, while augmented analytics platforms unlock new capabilities upon installation, turning non-technical employees into citizen data scientists will require significant training.

At a minimum, training should include the following.

• Data literacy
• Analytic methods
• The ethics of AI
• Using analytics in existing processes and workflows
• Applying analytics to business requirements
• Open collaboration methods

Keep in mind, the more automation that enters the mix, the more training is required. While this may seem counterintuitive, the idea is that the training content changes. Instead of focusing on how to use the technology, organizations will need to help workers understand how to interpret and apply insights based on context.

Businesses Are Increasing Investments in Data Analytics

According to the Microstrategy survey, 94% of organizations believe data and analytics solutions are critical for growth. However, only 3% of respondents can currently locate information in seconds, and 60% say that gathering the information required to make a data-driven decision can take several hours or even days.

As it stands, survey respondents ranked the following as the top benefits offered by data analytics:

• 64%: Increased productivity and efficiency
• 56%: Faster, more effective decision-making
• 51%: Financial gains
• 46%: Ability to identify and create new revenue streams.
• 45%: Ability to redefine existing business models and explore new ones.

According to a Bloomberg Research report on the state of business intelligence, few organizations are currently investing in the more “sophisticated” analytic tools such as unstructured data analysis and web and social media analytics, which remain in the “early adopter phase.” Instead, businesses primarily use BI tools for strategic planning and financial purposes.

Data Integration Tools

The Bloomberg report also revealed that data is the number one roadblock when it comes to analytics adoption and use. Many organizations still struggle to manage data, ensure accuracy, and even understand how to access their internal solutions. That same report also found that spreadsheets remain the most used business analytics tool. In contrast, a 2019 report from IBM found that the average company has only migrated about 20% of their data workload to the cloud, despite being aware of the benefits.

Data integration tools could help organizations unlock a ton of potential hiding out in their existing systems. According to IDC, the current state of analytics includes a lot of friction. Data workers use four to seven different tools on average to manage data and often resort to spreadsheets, which not only takes up a ton of time but could also result in errors and data compliance issues. To realize the most significant benefits of Big Data, organizations will need to find a solution that consolidates all data activities into a central platform, offers end-to-end traceability and data protection, and is easy for workers to use.

Building an Analytics Culture

The real power of augmented analytics comes from its ability to empower more people with advanced self-service solutions. However, just because there’s this “democratization of Big Data” happening doesn’t mean that the emerging AI solutions can magically transform how organizations work.

According to Statista, about 60% of companies with Big Data strategies were able to cut costs because of their investment. Yet, just over 40% said that poor alignment or a lack of agility caused efforts to stall. As with any new initiative—be it a microservices transformation or industrial IoT adoption—success hinges on establishing the right organizational culture.

The State of Analytics is Changing—Are You Ready?

In looking at the current state of analytics, it’s clear that organizations have overestimated their data capabilities for several years. While self-service analytics tools provide varying degrees of insights, many only provide business users with part of the story—offering little in the way of context for “why sales are down,” “why that marketing campaign bombed,” or “why raw materials suddenly cost 10% more.” While data scientists and IT pros can generate reports with more contextual information, those kinds of tasks aren’t the best use of their time.

Augmented analytics represents the future of analytics, business intelligence, and of course, Big Data. Still, organizations will need to rethink training, culture, and processes to ride this new wave.

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