Data Science vs. Data Management

Data Management has risen to new heights as a result of a shift in business perceptions of data. Besides, data Science is now an important part of data management, however, we always frequently confuse about data management and data science. Moreover, data scientists spend their time putting together the data infrastructure for data analysis and competitive intelligence, collaborating with data analysts, data engineers, and DBAs. However, in the rapidly expanding next-generation data industry, Data Management and analytics will be the key differentiators for market success, so Data Management and Data Science must collaborate. (data science in Malaysia)

According to a Forbes article, the worldwide data management and analytics market will reach $135 billion by 2025, according to an Everest Group analysis. Vendors in this sector have shifted from a function-to-process to a platform emphasis over time. Data is no longer seen as a consequence of business processes, but as the business’s nerve centre in platform orientation.

The Fundamental Difference Between Data Management and Data Science

The enterprise data acquisition, storage, quality, governance, and integrity are all under the supervision of the Data Management function of an organisation, which also oversees the formulation and implementation of all data-related rules inside that organisation. The Data Management team, on the other hand, only manages the data assets and is rarely involved with the data’s fundamental technological applications. All data is owned by the Data Management function. Peter Aiken discussed “prioritising organisational Data Management demands vs Data Strategy needs” in the webcast Data Management vs Data Strategy.

The Data Science function, on the other hand, conceptualises, develops, implements, and practises all “technical application” of data assets in an organisation. The term “technical applications” refers to the science, technology, craft, and business practises that involve corporate data in this context.

The Data Science team does not own any data; instead, they collect, store, process, and analyse it, then communicate data-driven results to the rest of the organisation for business benefits. The data scientist is a subject matter expert in Data Science and related technologies who advises businesses on data-driven strategies using highly specialised expertise (statistics, computer science, AI, and so on).

In practise, the Data Science function is part of the organization’s Data Management function. The Data Science team delivers to the organisation a set of basic technical skills for implementing best practises as defined by Data Management policies, processes, and guidelines.

Data Science Practices vs. Data Management Practices

With the volume and complexity of data growing at an exponential rate, data management has become one of the most critical components of corporate operations. Setting up data-related policies, procedures, roles, responsibilities, and strict access-control measures are all part of data management practises.

Company executives and operators are now debating the importance of a well-structured Data Management strategy that focuses on Data Governance for optimising business value. An enterprise’s Data Management team developed and conceived all the policies.

In their everyday data-related job, data professionals in various sectors of a company are responsible for implementing and enforcing all policies and guidelines. As discussed in Data Management vs. Data Governance: Improving Organizational Data Strategy, we highlighted Data Governance as an essential component of Data Management.

Although no management responsibilities are directly present at this level, strategic policies, procedures, and guidelines play a vital role in the implementation of data technology initiatives in the Data Science sector. To put it another way, organisational data strategists finish their work by shaping data management policies, procedures, and guidelines; then it’s up to data scientists and other data professionals to follow the policies and guidelines in order to keep the organisational data strategy blueprint intact.

In order to oversee the implementation of the enterprise Data Strategy through the use of controls, Data Management strategists will consider possible violations and fines.

Relieving the Data Scientist with Augmented Data Management

Five fundamental Data Science operations, including data integration, Data Quality, Master Data Management (MDM), Metadata Management, and Database Management Systems (DBMS), are fully or largely automated in a typical enhanced Data Management system.

The employment of powerful AI, Ml, or analytics techniques relieves the data scientist of the “drudgery of data preparation.” Typically, a data scientist spends roughly 80% of his or her time preparing data for analytics; these technologies eliminate that time-consuming task, freeing up time for more difficult analytics tasks such as model creation or data interpretation. One of Gartner’s Top 10 Data Analytics Trends for 2020 is Augmented Data Management.

Data Regulations’ Importance in Data Management and Data Science

Data legislation such as the General Data Privacy Regulations (GDPR) and the California Consumer Protection Act (CCPA) have given a new dimension to existing Data Management techniques that overlap Data Science. The new standards provide improved governance mechanisms, particularly in the areas of data privacy, data security, and ethics, but they also make the AI-powered Data Science platform more complicated. Now, data managers must consider not just putting in place tight data privacy, security, and ethics controls, but also the impact of modern technologies (AI, ML) on Data Governance.

These operations will stay parallel in the new world of regulation-centric Data Governance, Data Management, and Data Science practises, but they will intersect at times.

What is the end effect of such a collision? Mergers, acquisitions, and integration will occur among vendors and service providers.

Source: data science course malaysia , data science in malaysia

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