3 Reasons Why Data Management Leads to Business Success

Data management is one of the most important initiatives for businesses in 2018. Broadly speaking, data management includes the development and implementation of systems, policies, processes, and procedures that allow organizations to efficiently manage the information that they collect or generate in the course of doing business.

In 2018, organizations are generating and storing more data than ever before. Wal-Mart handles 1 million customer transactions every hour and imports them into a database thought to contain more than 2.5 petabytes of data. Advertisement firm Akamai collects and analyzes data on 75 million distinct events per day to determine how best to target its advertisements. More data has been collected in the past two years than in the entire history of the human race, yet only 0.5% of all collected data is ever put to a productive use.

In this piece, we’ll offer 3 reasons why data management leads to business success and why your business should track, measure and analyze more of your data starting today.

Where Does Data Come From?

Each organization is unique in the way that it collects, captures or generates data. An organization may collect data directly from the customer when they sign up for a product or service, or it may capture information from customers through their connected social profiles. Organizations that process paperwork may capture form data electronically and organize it into a database, or they may create a data entry or conversion process that translates collected data into a more useful format.

Sometimes, organizations create data of their own – sales organizations may keep track of sales performance metrics individually or by department, using this information to set future and measure performance. Some organizations implement a key performance indicator (KPI) system where employee performance is measured against pre-determined metrics that are perpetually tracked by the business.

The design and structure of a data management system should consider the data source and how the data will be entered into the system. Organizations that can automate the data collection process will save on labor and costs while reaping the benefits of data collection. Some organizations that traditionally collect data on paper have begun migrating data into the cloud, abandoning paper records in favor of modern digital formats.

How Does Data Management Lead to Business Success?

The connection between data management and business success relies on the effective implementation of a data management strategy. Organizations must determine what data to track and ensure that data is collected accurately and efficiently. Once an organization can collect data, there are many ways to leverage data as an asset and turn it into better business results. Below, we highlight three ways that data management can lead to business success:

Data Management Enables Knowledge Generation for Organizations

One of the key benefits of successful data management is that it allows organizations to develop knowledge internally rather than looking outside the company for expertise or hiring a consultant. The generation of knowledge can be achieved through something called the DIKW hierarchy.

• D is for Data. Data is the foundation of the DIKW hierarchy – it’s what organizations need to be able to capture if they want to generate knowledge from their activities.
• I is for Information. Data turns into information once it is given context.
• K is for Knowledge. Knowledge is what you learn when you conduct an in-depth analysis on available information and look for correlations and patterns that can explain trends or observations.
• W is for Wisdom. If you’re lucky, your analysis will provide simple, reliable and repeatable insight that you can use to drive results for your business.

Let’s match each stage in the hierarchy to a hypothetical example. Suppose we collect a bunch of data about how many calls the sales team is making per day, so for 90 days we track how many calls are made each day and how many sales result. For that data to be truly informative, it needs a wider context. To create that, we cross-reference it with individual call volume records, work attendance, and staffing data and other productivity data. Now we have all the information and context needed to conduct an analysis. Here are a few things we could try to determine:

• We can determine things like the average number of sales calls required per day to hit the sales target.
• We can find relationships between other activity metrics and sales results
• We can determine which salespeople are the most productive and optimize scheduling to ensure the company meets its target
• We can set better productivity goals for staff with a better understanding of what activity levels lead to success.

Data Management Drives an Omnichannel Customer Experience

At the heart of every customer-driven business is a robust and reliable customer relationship management (CRM) database that can be used to records, access and manage customer interactions. CRM software allows organizations to collect and database customer information and it allows agents to access and reference the customer’s file at any time.

A CRM tracks an organization’s entire relationship with a given customer – it includes their personal information, purchasing history, preferences, and notes from any interactions with the customer. CRM entries can include form data that allows companies to filter their customers across desired metrics. You could filter all of your customers who have spent over $1,000 with you this year and offer early access to a special sale, or view your lowest spending clients and target them with up-sell offers.

Organizations that can track and update CRM data across multiple touch points are able to provide seamless customer service interactions for customers while efficiently using data to drive sales and business success.

Effective Data Management Supports Legal and Regulatory Compliance

Depending on the industry, many organizations face stringent regulations surrounding their data management. There are many cases where a failure to manage data correctly can result in expensive legal issues for organizations. The new European General Data Protection Regulation (GDPR) places additional compliance requirements on businesses that collect consumer data. The regulation allows consumers to access data that businesses have collected on them and gives consumers the right to request erasure of personal data related to them.

These requirements place an additional regulatory burden on organizations that collect data about their customers. Organizations must support and maintain the capability to access data from the system, share it with authorized persons upon request and completely remove it from the system (erasure) if and when required.

Companies that collect individual medical data or conduct medical claims data processing are subject to the HIPPA legislation which requires them to ensure the security and privacy of patient data. Healthcare providers, medical claims processors and even software companies in the healthcare space may be required to comply with HIPPA.

Summary

Businesses around the world are generating and collecting more data than ever before, and an effective data management system is the first step to realizing the business success that can result from efficient handling of both customer and internally-generated data.

With effective data management, organizations can turn their collected customer data into valuable insights that inform business decision-making, offer a seamless omnichannel experience by tracking customer interactions across multiple touch points and support the relevant regulatory and compliance requirements.

Organizations that collect data on paper should not miss out on the benefits of digitization and computer data analysis. Document scanning services can enable organizations to digitize their entire backlog of docs for efficient storage, cataloging, and access, removing any and all barriers to an effective data management program.