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Data Analytics - Jul 05 2021

What Is a Data Maturity Assessment?

As businesses around the world use large amounts of data to generate business insights to support better decision-making. One of the crucial pieces of the puzzle of succeeding with this approach is how much, and how capably, a business is able to use that data. It’s vital to ensure that a data strategy gets to grow and evolve over time to maximize efficient use of data.

Data maturity refers to the extent to which a business makes effective use of their data. The more data mature a business becomes, the more data it utilizes in a meaningful way. A data maturity assessment is an integral part of this process. It helps businesses create a best practices roadmap for effective management of large volumes of data. With a data maturity assessment, businesses can create and implement an evolved data strategy to maximize the effectiveness of their data analytics process and to consistently deliver mission-critical business insight.

Let us take a look at some important elements of a data maturity assessment process.

Proving and Reporting Data Management Best Practices

A key component in becoming data mature is to implement and onboard data management best practices that help create a flexible and evolving framework for using data as business realities and requirements change over time. 

It is important to keep focus on teams before technology. Whenever you prioritize technology, you run the risk of creating a methodology that adopts to the technology instead of the other way around. The focus should always firmly be on your teams and business goals and the technology should come second, as a means of achieving an end.

It is also crucial to report and prove value early and often when it comes to data management. In situations where there is no guarantee of a long-term budget and questions about eventual ROI, data management best practices need to be framed, from the very outset, in such a manner that clearly shows its effect on all related value chains. Multiple short-term wins can convert into long-term realization of business goals.

A prime goal of any assessment is to help the organization quantify its growth and maturity. This can be beneficial in assessing a particular data management area, or to equip the organization for a new direction. Through proving and reporting data management best practices, organizations can achieve the following fundamental results:

  • Understanding the current state and preferred direction for enterprise architecture and infrastructure.
  • Understanding the preparedness for starting any organizational or technical change that involves information management.
  • Gauging the current state of organizational capabilities pertaining to data management, governance, and stewardship.
  • Assessing current state of technological and business capabilities regarding metadata management.
  • Assessing current readiness to develop a new BI initiative, like a data warehouse.

Scope and Capabilities of Data Management

As integral parts of the data management framework, it’s crucial to understand the relevance of each part of the process in order to accurately define the scope and capabilities of data management. You need to start by taking a look at the data storage and data integration methods in use. From there, it makes sense to move on to other basic aspects of data management such as data stewardship and data governance.

Through data governance and Master Data Management, one crucial result that can be achieved is an overall improvement in data quality. By enhancing overall operational effectiveness and driving insight-based decision making in business, data management can make organizations for agile. It can render it far easier to gain competitive advantage by clearly seeing, forecasting, and understanding market trends.

An astute idea is to first define your full understanding of the scope and capabilities of data management, followed by discerning the appropriate data chains that are directly linked to key business value chains. If your company has a non-standard or company-specific set of data management capabilities, it can make sense to gradually transition to a more standardized model of data management in order to align your data management vision with industry peers. This way, you can benchmark your results against other leading companies in your business area to gain useful insight.

Specify Maturity Levels and KPIs

A standard part of a data maturity assessment is clearly defining the terms. The assessment needs to clearly establish the current state of an organization and also concrete goals regarding where it needs to be in terms of data maturity. From there, the gap between the two states can be evaluated to create a roadmap for progress.

Here, it’s fundamental to establish current and target maturity levels as a quantifiable entity. This can be achieved through the help of Key Performance Indicators or KPIs. As a part of the assessment, different important metrics can be measured and used as benchmarks for the assessment process to track progress.

Track Company and Competitor Achievements/Progress

With clearly specified criteria for progress, the data maturity assessment can then commence based on concrete parameters. Using the specific KPIs, businesses can track progress in a quantifiable manner while also keeping an eye of competitors in similar states of data maturity.

While keeping track of progress it is also essential to keep refining and evolving the assessment process to hit pivotal milestones so that the integrity of the assessment can be preserved and accurate results derived.

Have questions? We help companies like yours, every day.

Email us at hello@nextphase.ai

 

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About NextPhase.ai

NextPhase.ai is a data cloud services provider specializing in Snowflake, cloud data management and analytics technologies. We accelerate enterprise digital transformation initiatives by leveraging our innovative cloud data management technology, “NextPhase.ai DATAFLO” to optimize and rationalize disparate enterprise data into relevant insights. “DATAFLO” is designed to automate the lifecycle of data management transformation using AI and ML along with expeditious on-ramps to the Snowflake data cloud infrastructure. NextPhase.ai provides a range of technology consulting services for the Financial Services, Biotech and Technology industry sectors combining our platform-based services, seasoned talent, and industry proven methodology so our customers can harness more from their data. We are a Silicon Valley based company with global presence having delivered high value service engagements for numerous Global 2000 enterprises.

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