Struggles For Data Quality & Data Governance In A Machine Learning (ML) Ecosystem

The high accessibility of data, advanced data science technologies, and improved computing power jointly make a lethal combination for data-driven results. With the open data economy around the corner, fine-tuned data governance capabilities will be the aim of many businesses.

 

Existing data management practices are centered on regulatory compliance and risk-free data sharing. In an open data economy, less risk of data sharing & high governance mechanisms is critical to success. As Data Governance resumes to gain prominence in data-enabled business models, businesses will spend in advanced data technologies like AI & ML development to attain compliance, quality, and security at scale, as per Managing Partner at KPMG, Bill Tomazin.

 

Data Quality anticipates more significance in the ML-focused, self-service analytics world as business users are not eligible to examine the quality of data in use. Companies now realize that unless Data Quality issues are dealt with first, their fundings might go high data volumes, channels, & unorganized data types have added to data management problems, mainly in areas of data governance & data quality, as stated by the author of the impact of data quality in the machine learning era.

 

Struggles for Data Quality in Digital Firms

 

 

Whereas multi-source and multi-type data has enhanced the business data troves, data management has become a severe problem due to bad data quality, data quality management resumes to haunt data management professionals, and they know that unless data quality problems are managed correctly, companies can lose the golden chance of deriving competitive intelligence.

 

Several researchers think that data quality concerns handicap the actual potential of data-driven businesses. The use of machine learning technology to present data quality challenges is still confined, though several industry leaders trust that ML can confront data quality issues head-on. The solutions rendered by advanced AI/ML solution platforms to manage data quality are often super economical & efficient.

 

Machine learning solutions currently can examine the quality of data assets, forecast missing values, and render cleansing suggestions, thus preventing the complexity and efforts spent by data quality professionals and scientists.

 

With data entry points growing each day, companies face hardship to gather and save the data efficiently. AI renders the chance to automate the data entry process via intelligence capture, thus improving incoming data quality. Excellent quality data improves the quality of marketing campaigns & predictive analytics.

 

Struggles for Data Governance in Digital Firms

 

Data quality's integral issue further complicated a business's data management complexity due to significant data volumes, unorganized data types, and disparate data sources. Whereas ML/AI-enabled systems continue to rise in digital firms, the lack of robust data governance frameworks can unravel unreliable and misleading data and unexpected costly overheads.

 

Below are some common & often debated data governance hardships facing AI/ML-enabled businesses.

 

The data access controls, such as who has access to what data?

The consistency, reliability, and accuracy of data.

The existing data storage & integration infrastructures — but are they sufficient?

The security problems surrounding data movements with & without companies.

The executed Data Governance Plans — what is scarce?

 

The Forbes author has discovered the integral issues of a data governance plan in an AI-enabled data management surrounding, including data security, data integrity, data governance, and data integration. Besides data quality, consistency, access controls, and storage-integration techniques, this blog also examines the unlimited potential of data-driven insights in an ML/AI-enabled business ecosystem.

 

Machine Learning 'a Savior' for Data Governance

 

Data Governance & Machine Learning article reveals the existing status of AI adoption in the sector. Whereas the C-Suite Executives are eager to adopt AI-powered data management solutions, while the tech experts are sure that AI/ML tech adoption might remain a remote dream unless sound Data Strategy plans are in place.

 

In the blog titled Metadata & Machine Learning in Data Governance, the author debates that in a post-GDPR world, metadata plays a key role in data governance, as witnessed by the increase of topical discussions on metadata's role in data governance. Previously, Gartner announced that by 2020, 50 percent or more data governance policies would be metadata-driven.

 

Any contemporary business must have a correct data management infrastructure in place to leverage the advantages of 'tech-supported decision making' enabled by advanced AI/ML systems. However, for these advanced technologies to deliver competitive intelligence, the data's flow must be tracked, controlled, and supervised throughout its journey in an end-to-end business analytics system.

 

Financial Sectors ~ A Famous Data Governance Use Case

 

 

One of the authors in the article stresses that ML solutions' success is closely intermingled with data governance techniques at work inside a business. At present, while general US businesses are busy applying CCPA or its several variations across the nations, the financial industry seems to have found a convincing answer in ML-enabled solutions.

 

As digital firms count solely on the data's power for their operations, data governance plays a significant role in delivering a competitive edge. Data, twined with advanced technologies, can push a business to the tip of success if used correctly. But as disclosed by a KPMG International report, around 2200 business executives are concerned regarding the governance hardships of data on a shared platform, as in manufacturing or medical businesses.

 

Data & Business Teams Playing Team Sports

 

A DBTA blog focusing on cost justifications for data quality technology investments in AI/ML systems reveals that the main source of wrong quality data is sales departments, where the sales reps often enter incomplete or incorrect data on the CRM system. The bad data can easily cultivate other departments or functions through linked applications or processes. The fundamental problem of data management is the lack of communication between the business & IT staff. The business employee thinks data is an IT problem, whereas the IT department thinks clean information is the business staff's responsibility, which creates the data.

 

At a recent business summit, top leaders acknowledged the significance of a data strategy for data-driven insights and failed to share their success with a clearly defined data strategy. They felt that data practices must include the business & data staff as part of the team. 'Translators' would serve as connective tissue to connect the communication gap between technical and business professionals. HData Listed One of the Trusted Big Data Analytics Companies by Top Mobile App Development Companies.