7 Steps To Build Confidence In Your Data

By Vincent Tang, Regional Vice President Epicor

The current market conditions have pushed businesses to place digital transformation at the top of their priorities. With the help of tools and resources, organizations are looking to build resilience in the face of rapid changes to focus on serving the needs of their customers.

The success of every modernization and business transformation initiative rests upon the quality of its foundational data. However, after making significant investments in technologies, many businesses find their system data to be subpar, which makes it challenging to understand their business fully and effectively engage and serve their customers.

According to an IDC report, digital transformation spending in Asia Pacific will steadily accelerate and is expected to hit US$ 1.2 trillion between 2020 and 2023. As the region gears up for rehabilitation, businesses will focus on implementing recovery framework and technology-based new normal operating and business models. To optimize technology investments, companies need to ensure that they are building on quality data.

So how can businesses get a handle on the information component of information technology? How can enterprises ensure that their modernization efforts are standing on solid data?

Below are seven tips that can help companies successfully craft and execute a plan designed to clean data, and more importantly, ensure that data quality is maintained.

1. Start by crafting and communicating a data strategy vision.

For almost any endeavour, crafting a compelling, yet simple and easy to support vision that stakeholders can rally around, is a vital first step. For instance, when creating a data strategy vision for customer data, businesses need to define:

i. Who their prospects and customers are.

ii. What they purchased and how much they paid.

iii. What their post-sales experience with the business is like.

These three elements follow the customer lifecycle, from pre sales to post sales activities, but for companies that have other major milestones in their customer lifecycle, additional elements can be added to the vision statement.

2. Identify the specific data elements required to support your vision.

Information systems are capable of housing large volumes of data, and it is not uncommon for systems in use for many years to have hundreds of data elements or fields. When using data from Customer Relationship Management (CRM), the actual number of data elements required to “know who your prospects and customers are” are likely much lower. Define what data should be included on the list, but like a data strategy vision, keep it simple and then repeat the process for each data strategy vision element

3. Assign owners to clean and sustain the data for each element.

Aligning the data elements with each stage of a business process makes it relatively easy to identify good candidates for improving and sustaining data quality. For example, prospect and customer records are first created and used by Marketing and Sales, both of which are good functions for managing CRM data for knowing “who your prospects and customers are” Business Operations and/or Finance teams typically operate a company’s ordering and billing systems, and these teams are good candidates for managing entitlement data, or “what they purchased from us, and how much they paid.” Support teams often have the best understanding about customer experience post-sales, and are good candidates for managing support data, or “what their post-sales experience with us is like.”

Regardless of how the company is structured, referring to each stage of a business process as well as data elements used at each phase is an effective way to determine the best team to entrust with cleaning and sustaining the data for each element

4. Determine what resources are needed for each data element to be successful.

Now, the hard work to clean and sustain data begins. For smaller companies with simpler needs, the only resource needed may be the time employees require to clean the data manually. This is feasible for businesses with a relatively simple product portfolio and a small number of customers.

For larger companies with tens of thousands of customers and a complex portfolio of products and services, additional resources may be required to make timely improvements. For example, businesses with existing CRM platforms and already have updated elements, like company names, key contacts, and hierarchies, but may require enrichment on several other data elements. Moreover, complex product portfolios can require product mastery to track purchases and payments effectively which is a capability that might need outside expertise to develop.

5. Learn how to leverage IT effectively on your data quality journey.

Business functions are best suited to define and meet their data quality needs, but the task of ensuring that systems are available to securely store, manipulate, and present the data is best left to the IT team. Systems records, data elements, and access rights to data should be under the care of IT teams. They should also be responsible for developing, implementing, and operating data integrations that reliably and securely transport data between systems.

6. Measure progress using empirical success criteria.

Establishing success criteria should be relatively easy with defined data elements. Success means that the data identified as essential is clean. However, measuring attainment can be a little more challenging, especially for companies with large data volumes. For these organizations, crafting system queries that sample a statistically significant volume of data can help make the task more manageable. Building and generating reports that support data quality assurance is typically something IT can assist with while business functions can assist through assessment and review of these reports.

7. Make maintaining data quality a regular part of operations.

Data begins to decay the minute a company stops its data quality management efforts. Sales representatives may forget to update a key contact’s new phone number, or a duplicate account mistakenly gets created in the CRM or a new product is introduced without a corresponding product ID. Avoiding data decay requires ongoing quality efforts that are woven into routine operations.

Maintaining the quality of company data requires time, resources and effort but with patience, clear vision and delegation of defined responsibilities to the right teams, enterprises can build more confidence and trust in their data.

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