Organizations keep messing up their data insights — here’s how to fix it

The current economic downturn and global disruption from the pandemic created a “digital awakening.” Boards and C-level executives are accelerating digital transformation initiatives to drive efficiency, growth, business resiliency, and remain relevant and competitive in the new digital normal. Now, to make effective decisions these executives need data. To make effective decisions in a timely manner these executives also need to automate the manner they capture, process, analyze and draw insights. 

So, if we all agree that timely access to rich insights from data is the holy grail then the obvious question we’re left with is: “what do I need to reach this state of digital nirvana?”

In this piece, I’ll take a hypothesis-based approach to focused outcomes, then back into the type of technology that can help executives achieve the value badly needed to navigate the current crisis while positioning for the rebound.

Most of us likely agree that insights from data is extremely beneficial, in the immediate and long term. Then why do so many organizations still struggle to capture, visualize, understand, and optimize business-critical information from the moment it flows in?

What’s actually going on?

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It’s not that industries don’t understand the value of data, particularly as artificial intelligence (AI) and augmented analytics have gained traction globally. 60% of CIOs say that data and analytics will affect their businesses in the next three years, and 73% of companies are planning to invest in DataOps initiatives to support AI and machine learning initiatives.

But to leverage any of these advanced analytics technologies, organizations first need to capture the data. This includes structured data, including websites, business and desktop applications, and databases. But even more important is unstructured data, since these accounts require more overall data than structured. Unstructured data is the content found in documents and emails, for instance. 

Once we’ve ingested 100% of the available structured and unstructured data, the data must be orchestrated into appropriate workflows to feed downstream systems.

The typical organization will use several disparate applications to process a single transaction. And in many organizations humans serve as the ‘connective tissue’ among these disparate applications. This is expensive, takes time and is prone to error. 

Let’s look at a typical customer onboarding process. This likely requires an initial digital channel feeding a CRM triggering credit checks, bounces off a decision-engine to initiate Know-Your-Customer (KYC) actions, sending notifications to the customer providing updates and requesting documentation while another application’s partitioning the user account. 

To make this happen seamlessly, an automated end-to-end digital customer workflow can be designed to orchestrate the flow of data, eliminate errors, reduce cycle times and increase compliance.

Automation can also accelerate the generation of data insights by rapidly aggregating different types of data — comprising business data, and data from different channels and sources, including operational and processing data, customer data, customer or stakeholder feedback, etc. The result is a more accurate and faster way to visualize business intelligence. 

Finally, every organization wants to maximize the value of their data. A great approach is to “open it up” a bit through data democratization using the right tools — empowering employees to spend more time engaging with and exploring the data to gain insights they can use in their roles. 

Data analytics can change the game

Banks, insurance companies, transportation and logistics firms, healthcare companies, government agencies and more — every day, an avalanche of data pours into organizations across every industry. This data comprises a variety of formats and originates from multiple sources.

A number of organizations still struggle with information silos, inefficient legacy infrastructure, and uncaptured and/or unstructured data. But increasing numbers are now successfully automating data capture and transformation into the right format.

Lingering headaches that are common are high error rates from the merging of information entering the organization, poor documentation and different rules requirements. All of this can contribute to production or service delays and, ultimately, frustrated users.

This is where analytics capabilities part of a larger, integrated intelligent automation solution can be game-changing. Automation and workflow create a new ‘digital frontier’ removing friction resulting in higher efficiency levels, but when analytics are added we ultimately enhance decision-making.

The business value of integrated intelligent automation and analytics is enhanced oversight of key business processes, streamlining workflows, pinpointing the likelihood of bottlenecks or service interruptions before they happen and speeding the delivery of critical business data and decisions.

Automation helps you make the most of your data

The most elementary application of automation to your data can provide answers to questions including: is the data we’re capturing showing increased errors, and if so, why? Can we isolate user productivity by department and determine where extra training could be beneficial? Are our workflows processing data outside of our SLAs? If so, what’s the surplus?

Analytics is about uncovering patterns, particularly unanticipated ones, and helping organizations use those real-time insights to act upon that information quickly and proactively — and predict future potential issues.

Unfortunately, according to IDC, only 10% of usable data is used for analysis. It’s true organizations have become quite adept at data collection, even very large amounts. But many struggle with transforming the massive amounts of information acquired through intelligent automation into something understandable and actionable so they can make better business decisions.

This brings us to essential tools every user tasked with maximizing the value of organizational data should have. It starts with self-service, interactive, web-based dashboards and visualizations that don’t require IT to build new reports, modify queries, or perform coding, syntax or scripting.

Below is a list of the most important key dashboard configuration capabilities. Users should be able to:

• Uncover real-time process trends through data visualization and manipulation to improve performance
• Drill down to identify low-confidence fields and set the right actions to improve process quality
• Filter batch and document numbers by class and pinpoint bottlenecks
• Evaluate productivity statistics teams — number of batches, documents and pages processed per time variable, team or process
• Use predefined operations metrics to quickly view and accurately measure and improve performance
• Get objective performance monitoring of human operators, business processes, and software performance
• Use a mobile or desktop view from any device
• Protect data on-the-fly and apply security roles to protect data
• Drill down to the lowest level of process data to account for, track and trace processes

Final thoughts

Many organizations are leaving a great deal of insights on the table when it comes to their data. There’s never been a better time to move beyond the boundaries of traditional systems and redefine what analytics means as digital transformation accelerates in the ‘new normal’. 

Integrated intelligent automation connecting systems, data, and people to achieve outcomes combined with powerful business intelligence is the key. The ability of organizations to garner real-time insights from this activity is the path toward realizing the agility and resiliency successful organizations need to thrive today and in the long term.