Data Quality Trends Making Waves in Data Management

With stats flying around every day about how much new data is being generated by Internet of Things (IoT), social media, mobile devices and the cloud, it is no wonder that we feel swept up in this digital wave and feel there is no easy way to swim out.


One thing remains constant though, the quality of the data (no matter how much you have) actually matters. And focusing on managing the core, master data of your organization with a trusted data foundation is what matters most.

We’ve pulled together some of the important Q&A’s around the key trends in data management, how companies are leveraging these trends for growth, and the impact of these trends on companies.

What are the Top Three Trends in Data Management Today?

The top three data quality trends in data management today are data warehouse modernization, machine learning algorithms, and modern data hubs.

Data warehouse modernization is an ever-growing trend that spans across technology and business and includes a focus on applications and tools, user technique, and business requirements and motions. Modern data warehouses are not merely coded – they are crafted with a greater need in mind and require attention to proper platform selection based on business needs and data-driven structure.

Advances in automation are providing increasing opportunities for machine learning algorithms to take center stage within a company’s data management strategy. Developers are able to apply algorithms to tasks that exhaust time and resources. While removal of common processes may seem like a threat to the futures of developers, it offers space to focus on productive and innovative tasks that were formerly lost in rote processes – the kinds of processes that require human thought, creativity, and collaboration.

The growth in demand for structured data warehousing and management has led to a wide array of modern data hubs that offer various balances of advanced tools. These hubs keep data moving and provide a holistic approach to data management, all the way from curation to orchestration. The trend in offerings currently available and still in development supports the expectation that the need for structured and dynamic data management will not lose its relevance any time soon.

How are companies successfully implementing/leveraging these
data quality trends?

In leveraging machine learning automation, companies transform the way that they can look at the behaviors of their customers. For example, a manufacturing company can automate the process of scrubbing through their database with an output of actionable knowledge around purchasing patterns and product lifecycle.

Modern data hubs and warehouses provide companies with immediate availability of data, support security and ethical compliance, and set a strong foundation to build on as data collection expands to facilitate more predictive or prescriptive analysis. This is one of the powerful benefits of implementing and leveraging these trends: companies are able to step back from staring at rows and rows of data and instead look at the output and trends to determine how to move the business responsibly
and strategically.

In some cases, companies will see data stories that they were completely unaware of and that will significantly transform future profits and customer relationships.

Protip: When strategically implemented and successfully leveraged, the application of both of these data quality trends to a given company’s ecosystem provides room for standardization of data collection and organization.

How do these data quality trends disrupt the business as usual routine that many companies follow?

These trends challenge businesses to review, criticize, and correct processes that are broken, unproductive, and wasteful within their business as usual routine. The idea of implementing a master data management strategy can seem overwhelming, both from timing and resource management perspectives.

Long term, the transformation process is cost-effective. It removes the need for warm bodies running recurring, manual processes and opens up the opportunity for those skilled individuals to do what drives the business and innovates. Investing in the implementation of modern trends in data management may seem disruptive, but it pays off in the opportunities it creates for new business insights and masterful
data management.

How does the lack of a data quality solution impact an organization?

A lack of data quality solution within the current market can set back or crumble an organization. One of the biggest reasons this can be so destructive comes down to data protection. With the relevance of quality data collection, management, and analysis, checks and balances have entered into the mix to ensure that the data collected is properly secured and handled. Any organization lacking sufficient data security can end up in significant legal and/or ethical trouble.

Another data quality concern lies in trusting manual processes to manipulate or populate data. Electing to implement automations that rely on rules and structure to process data eliminates the potential for human error and adds efficiency to the organization where resources were previously held up in rote tasks.

Is data quality an IT only thing? Or are you seeing the business side show more interest in data quality initiatives?

Data quality is not an IT-exclusive concept or measure. It is a business-wide responsibility, and initiatives should occur from the moment data is captured until the moment motions are driven by the stories the data has to tell. It requires that every part of the business process subscribes to a data management strategy that considers security, data hygiene, and standardization in order to function holistically as a balanced ecosystem. Historically, the relationship between data support and configuration roles such as IT and front-line productivity drivers such as Sales experience a disconnect in the shared responsibility of data quality. More recently, there has been a push to challenge that disconnect and drive unity in ensuring that data maintains its quality from beginning to end.

It is clear to see, by developing a core foundation of trusted, quality data; you too can drive growth strategies for dynamic data management, enable enhanced customer experiences by studying customer behavior through data and help build the data-driven culture that is required to stay competitive today.

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