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How to Build Your People Analytics on a Strong Data Foundation

Jan 31, 2022

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The strength of your data foundation can make or break your business.

If you had a factory whose main raw materials were inaccessible, misused, of poor quality, or painstakingly sourced from multiple locations daily, you’d be out of business before you started.

It’s the same with people analytics. Data acts both as your raw material and your foundation for people analytics. You need to feed data into analytics to produce the insights needed to make the best decisions. You also need to begin with data as the basis for your people analytics infrastructure.

Why do you need a solid data foundation?

  • A sound data foundation reduces risk and inefficiency by ensuring quality input and output and optimal use of resources. It cuts down on time spent finding and cleaning up data, so you can spend more time gleaning insights.
  • You want the truth, the whole truth, and a single version of the truth. You don’t want multiple answers to the same question, frustrating your decision-makers and creating distrust in the data and unsound insights. A solid data foundation makes it easier to arrive at a common understanding.
  • A common understanding of data definitions, dimensions, and structure puts everyone on the same page, streamlines the process, and creates a framework everyone can believe in.

So, what is a robust data foundation?

Characteristics of a Solid Data Foundation

5 keystones on Data Foundation - Accessibility, Trustworthiness, Readiness, Timeliness, Security,

A robust data foundation has certain irrefutable qualities that support decision-making and, by extension, positive business outcomes. They are accessibility, trustworthiness, readiness, timeliness, and security. Let’s see what these qualities mean and how to achieve them.

1. Accessibility.

You can’t use what you don’t have access to.

Data might be from various internal and external sources and may be structured or unstructured. But if it’s scattered in different formats in multiple systems, each requiring a separate login, you can say goodbye to quick and easy access.

Efficiency will also take a hit if you waste time and resources finding, collecting, and summarizing disparate pieces of data manually.

To improve accessibility, first, identify what data you need and how you will make it available. Then, consolidate data in a single source to have consistent access and a unified view.

Besides improving efficiency across the business, it will also improve collaboration and encourage a unified approach.

2. Trustworthiness

For your data to engender trust, it must be accurate, complete, consistent, and verifiable. Regardless of the source, what matters most is its quality.

Data collected using different methods with limited validation is inconsistent or incomplete and must be cleaned. Nobody has perfect data, and that’s why we have data cleansing tools.

The data cleaning process involves preparing, validating, and reconciling data.

Data quality begins at the source; therefore, cleaning should originate there.

One of an enterprise’s data governance council’s top responsibilities is a framework for validating data at its source. They should appoint data stewards throughout the organization to make that happen.

Inaccurate data can severely impact your analytics results, and if leaders rely on the resulting insights to inform business decisions, the outcome could be dire. Garbage in, garbage out!

3. Readiness

Your data must be complete, available, and presented in such a way that decision-makers can understand it and take action. Define parameters, so you know what metrics will define success.

Two processes help to improve data readiness: data aggregation and contextualization.

Data aggregation involves gathering data from multiple sources or databases and presenting it in a comprehensive, summarized, easily digestible format ready for analysis.

Contextualization is organizing data in a way a user can easily understand.

It groups related information together to create a useful picture or story identifying trends, patterns, and correlations.

4. Timeliness

There’s no time to wait. Data must be available when you need it to ensure relevance and prompt action. Any lag between when a user requests the data and when it is available comes at a cost to the business.

Availability of real-time data is crucial. No one wants to make decisions based on weeks-old data defined by conditions that no longer exist.

This is where the value of “currency” comes into play.

Currency is the degree to which data is current with the conditions under which it was modeled.

It measures how current the data is to avoid making decisions based on old news. Quality data is not only clean and accurate but current.

5. Security

A vital aspect of data management is data governance, which sets standards and policies for managing data throughout its life cycle—collection, access, storage, processing, and disposal.

It also includes compliance with external standards by industry and governing agencies.

Thirdly, it governs what data each person can access.

It is imperative to do everything you can to secure data and protect privacy.

Your employees, customers, suppliers, and clients expect you to handle their data confidentially and with care. It’s what they deserve and what will earn their trust.

As an organization, you must eliminate or mitigate the risks associated with exposure of sensitive data, unauthorized access, and security breaches.

It’s Worth the Effort

If your data does not possess these five qualities, it is a liability that exposes your business to risk and inefficiency. For your people analytics to produce the insights and ROI you expect, you must build a solid data foundation.

 


About Pixentia

Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.

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