Member Article

Adapting to new digital demands: 4 steps to achieving quality data

Digital transformation efforts have accelerated in the wake of COVID-19. Many of those initiatives are heavily dependent on high-quality, trusted data. Quality information ensures a better understanding of the consumer, better decision-making, and smoother daily operations.

While data is essential in today’s environment, many companies are struggling to trust their data due to a high degree of inaccuracy. According to Experian research, on average, organisations suspect 29% of current customer and prospect data is inaccurate in some way. Information is often incomplete, spread out across multiple sources, and difficult to manipulate to gain meaningful insight.

That level of inaccuracy affects the organisation in a broad number of ways. To start with, organisations face wasted resources and poor analytical insight, which many businesses cannot afford at this time. However, it also affects key initiatives.

Poor data negatively impacts the customer experience and the success of new data-driven programs. The level of poor data has become so pervasive that only half of organisations consider the current state of their CRM or ERP data to be clean, not allowing them to leverage it fully.

Organisations have often underinvested in data quality. It isn’t always viewed as the most cutting-edge area of data management, but high degrees of inaccurate information can drag down initiatives. It is important to set out a plan for how to improve the quality of your data to better adapt to new digital demands. Here are four steps to get you started.

Outline the business benefit

Determine the business purpose for using your data and align your definition of quality around that. Ideally, data is not being cleansed for the sake of having clean data. Once you have a specific business benefit to achieve, you can develop specific KPIs that are relevant for your organisation. Track these KPIs over time.

Having metrics in place will not only help you assess the effectiveness of your programme but will also help you define a clear ROI for data quality improvement plans and help get senior stakeholder buy-in for your initiatives.

Assess the state of your data

Before you can get started on any initiative, you need to understand what you are up against. You need to map out a flow of your information and understand the state of your current assets.

Data profiling is a powerful way to analyse vast quantities of data systematically. It can tell you the accuracy of specific fields, the level of completeness, specific outliners and anomalies that may exist, and much more.

By profiling your data, you might see that a certain number of records are missing critical pieces of information or that there are duplicate records for many people. Data profiling can also determine if the content of the records is a date, text, or alphanumeric, helping you to identify inconsistent formats within the data.

Once you have that outline of what is wrong with your data, you can map that back to your business benefits. Do you have a specific data set you are trying to leverage that is largely inaccurate or incomplete? If so, look to solve those problems through bulk cleansing, new collection processes, and validation tools that put a barrier in place and prevent inaccurate information from even entering business processes.

After fully understanding the problem, you can start to chip away at issues based on your priorities and available resources.

Develop a data culture

A data-driven mentality must be embedded across the entire organisation. This starts with the CEO and the office of the CDO, but it also extends to all business users that interact with data, regardless of whether they carry a data job title.

Organisations are rapidly accelerating into an era of data democratisation, allowing business stakeholders to experience frictionless passage to the data assets they demand. They are also providing access to hands-on technology and analytical tools to interpret large volumes of data. With those changes, is this now the opportunity to change the organisations’ view of its data? By placing data in the hands of the business user, why not also make them accountable for its current and future state?

By assigning goals and metrics to improve the overall hygiene of the data estate, a broader group of stakeholders will get a sense of ownership and accountability they cannot ignore. This, in turn, encourages investment into employing the right systems, controls, training, and feedback mechanisms to ensure data accuracy does not erode over time—increasing the overall trust in data

Invest in technology for today’s business needs

Keeping in mind the changing usage of data and its importance, organisations need to reevaluate their technology investments. With tighter budgets and resources, decisions need to be made around the best tools that can help not only technical users but the increasing array of non-technical stakeholders who need access to data insights. Organisations need to consider more aggressively ease of use as a core component of their tooling. They also need to consider reuse and leverage machine learning, which can help automate processes for users who may be less technical.

Empowering business users to understand the data better is essential. And this can be done through the use of easy-to-implement, easy-to-use tools designed to help businesses maximise their data insight and build trust in their information. Implementing the right technology encourages better collaboration leading to better data insights across the organisation.

Tools such as Experian’s Aperture Data Studio, which combines self-service data quality with globally curated data sets, can do a lot of the heavy lifting when it comes to turning data from various sources into a single trusted source of information.

Whatever your business purpose is for your data, effective decision making starts with good data quality.

This was posted in Bdaily's Members' News section by Mike Kilander .

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