Data Analytics

Assessing Data Analysis And Maturity

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These days, it is not a question of whether companies are collecting and analyzing data on their execution of operations, but rather how effectively they are doing so. Companies spanning industries are all prioritizing their data analysis capabilities. They seek the real-time insights enabled from complete data visibility, which can optimize their processes.

Before organizations can begin gleaning insights from their data, it is essential to assess their data maturity level. Organizations that successfully gauge their current data maturity level can identify and address problem areas, helping to develop their data analysis capabilities into a more powerful resource. There are three stages of data maturity.

Stage 1: Basic

Those at the beginning of their data journey have recognized the need for collecting all types of data. They often do so by manually pulling it from multiple disparate sources to produce both spreadsheets and business intelligence reports that detail various findings — a process defined by tedious collection, input and aggregation of data by hand, most often due to siloed systems of reporting. Companies at this stage are typically defined as reactive, in the sense that they collect historical data from various functions (like customer relationship management, human resources and supply chain) and analyze it to find out what has already happened.

While problems and inefficiencies can be certainly identified from the data and then shared, the actual solutions are left up to individual business units and store-level teams without cross-organizational involvement. There is no single data platformused to consolidate data for cross-functional analysis.

Consider a supply chain investigator receives a 70-page spreadsheet showing biweekly delivery errors (i.e., items that the warehouse listed as packed but were missing upon receiving the delivery) for a specific destination. The spreadsheet is issued biweekly, so the investigator must check two weeks’ worth of manifests to identify the missing items and their drivers. After two days, they identify four drivers, all of whom work for a third-party logistics vendor who delivered shipments with product missing. They contact the vendor, which grants them access to its drivers’ logs in another application. Finding nothing suspicious, they check the vendor’s GPS data (in yet another application) and find that the drivers of the trucks with missing items all made unauthorized stops en route to their destinations. In the time it took the investigator to identify wrongdoing, another $10,000 worth of product turned up missing from the drivers’ shipments, and they are now untouchable.

Stage 2: Evolving

Stage two’s evolution of analysis and maturity begins with the consolidation of data from separate sources into a single location or data warehouse. While the consolidation still often requires manual collection, it allows reporting to share a more holistic view of organizational data as well as automation. Companies in this stage typically begin to identify big-picture trends and best practices. Workflows tend to be siloed across the organization, leveraging emails as collaboration tools to communicate systematic reports as links or attachments. Responses and actions to identified problems are more frequently followed up on.

The above supply chain transportation fraud would look different if the company were in stage two. While the investigator would still need to review everything somewhat by hand, they would save significant time by having the misdeliveries, drivers’ logs and GPS data in an exception-management system. Rather than switching between different applications and vendors, having all the necessary data together drives efficiency. Having the system pull only certain records based on “business rules” further drives efficiency and accuracy.

Stage 3: Advanced

In the advanced stage, an organization’s data has been deeply integrated into its culture and incorporated into business decisions across all organizational levels. Decisions are based on data analysis outcomes, rather than one’s gut feeling or biased view of a report.

Manual analysis and interpretation processes are now automated — meaning that data is now automatically consolidated, integrated, analyzed and interpreted. If the organization leverages an advanced analytics solution like prescriptive analytics, its employees also receive corrective actions in plain text for responding to any problems that the data uncovered. Thanks to a push mechanism, no one needs to “remember” to look for results — the opportunities leverage the workflow to identify the best person to solve each issue at hand.

This advanced stage empowers a simpler, faster investigation for the aforementioned shipping errors case. If the organization was in stage three, the investigator could have avoided any lengthy manual analysis. Instead, their organization’s analytics solution would have detected the missing products long before the delivery-error report came out. It would have automatically analyzed the GPS data and misdeliveries and correlated the missing items with trucks that had made unauthorized stops. The investigator would receive an opportunity stating: “Trucks numbered 112, 31, 44 and 503 made unauthorized stops and arrived at their destinations with items missing. Interview the following drivers for potential fraud.” This simple, automatically delivered opportunity would empower the investigator to confirm and stop the fraud before losses could increase or the drivers could resign.

How To Get Started

The widespread evolution and adoption of advanced analytics is causing organizations to increasingly reevaluate their current level of data maturity to determine how they can upgrade their capabilities. Organizations can get started on their path to the advanced stage by putting the following steps to action:

• Conduct an internal assessment of current data analysis processes to develop a baseline of how advanced data maturity can help the business. Remember, the key here is finding where automated intelligence and digital decisioning can replace manual labor.

• Create a set of goals and action items. Organizations should identify what they want to accomplish with advanced data maturity and create a clear map to ensure ROI.

• Invest in an advanced analytics solution that streamlines data analysis processes. All data will be analyzed on one platform and organized into simplified reports that provide actionable insights.

• Thoroughly review the insights and apply them to business operations workflow.

With these tips, you should be well on your way to optimizing your data analytics processes.

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