Data Management, Analytics, And A New Era of Growth For Midsize Companies

Kristin McMahon

Money, time, employees, and resources – these are in short supply for a lot of midsize businesses. But many of them are overcoming those limitations by tapping into their abundance of data to channel their passion, genius, and perseverance into opportunity.

Let face it, running a business without managing data as a valuable asset no longer makes sense.

According to IDC’s Dan Vesset, group vice president of analytics and information management, and Ray Boggs, program vice president of small and medium business research, effective data management provides a new source of competitive advantage and revenue. They wrote in the IDC analyst connection “The Growing Value of Data Management for Midsize Businesses” that it is critical to evolve “into an intelligent enterprise, where data is key to business progress assessment and strategic, operational, and tactical decision-making.”

Data management = Accurate analytics = Business growth

Unfortunately, an inherent lack of data coordination, integration, and access is keeping many midsize businesses from achieving competitive advantage – which is a dangerous inhibitor to growth. Scattered customer information leads to poor sales effectiveness and low customer satisfaction. Unstandardized data causes internal misunderstanding of what data is truly saying. And misaligned information impedes strategic decision-making.

All too often, people are wowed by shiny new performance dashboards, data visualizations, and data-hungry applications. But they rarely think about the foundation for deriving value from these digital investments: data.

Even if they were knowledgeable about data management in the past, things have shifted so much over the last three years that the rules have changed. There are more data sources and raw information available than ever before, and they all have to be connected and flexible enough to allow businesses to move with the warp-speed pace of an increasingly digital world.

Three steps towards maximizing the value of data analytics

It’s time for midsize businesses to step up their data management game, but this doesn’t mean they have to break the bank or trade all of their existing analytics tools for the latest technology. What’s needed is a heightened focus on three critical areas of data management.

1. Take stock of all existing data

To build trust in analytics tools and insights, businesses need to know what knowledge their inventory of existing data is hiding. This can be acquired with two critical steps:

  • Data profiling: Explore information available in current data sources to find gaps in information gathering and the range of organizations, regions, and customers they cover.
  • Data cataloging and metadata management: Categorize the data and organize it logically in a repository or (as I call it) a data library. This approach helps everyone who contributes, moves, accesses, uses, and touches the information to understand what the data means and how it can be best applied.

2. Optimize data use

One of the cornerstones of data analytics is the ability to continuously improve data quality for all data domains, including customer, product, HR, and supplier sources. By transforming, merging, standardizing, and cleansing data, the business is one step closer to providing insights that every decision maker can trust, especially when they are bubbled up into a report.

Establishing rules and policies around data use can improve how every employee – from the leadership team to the production team – uncovers insights quickly and acts on them. For example, the company can dictate which actions are allowed or not allowed in a given application. There could even be hard-and-fast definitions on anything from customer criteria to business metrics.

With this level of governance, companies can:

  • Define, standardize, and correct data from any source, domain, or type
  • Fill data gaps by enriching it with internal or external data sources
  • Match and consolidate data by embedding duplication checks directly into workflows or applications
  • Check the quality of datasets at any time, in real time, and before analyzing, moving, or integrating data

3. Ensure the security of master data

Increasingly, growing businesses are capturing data that is more sensitive and distributed across their own IT systems as well as, quite possibly, their suppliers’ and partners’. The level of organizational exposure requires security measures that protect the company from risks such as information loss, unauthorized access, and data manipulation by bad actors.

Business can define data performance metrics and monitor master data processes and activities against them. With automated notification based on those metric thresholds, root-cause analysis can be performed by drilling down to individual levels of data objects, change requests, workflow steps, and users to identify operational inefficiencies and outcomes.

A foundation for business growth built on data consistency and trust

Data management is not a one-time project. As their use of digital technology matures, midsize businesses must treat data as an asset that requires ongoing and continuous maintenance and attention.

With this data management mindset, users can trust that data precisely pinpoints everything from potential cost savings and revenue growth opportunities to gaps in regulatory and policy compliance. And more importantly, it can help deliver the fast, accurate, and innovative outcomes that customers expect from the brands they love.

Discover the ever-growing importance of data management as a strategic capability for your growing company. Read the IDC analyst connection The Growing Value of Data Management for Midsize Businesses.”

For a first-hand view into SAP solutions for small and midsize businesses, visit www.sap.com/sme.


Kristin McMahon

About Kristin McMahon

Kristin McMahon is a solution marketing director for SAP Enterprise Information Management (EIM) solutions with expertise and focus on data services and data quality tools.