Leveraging Data Management Techniques to Make Use of Data Assets

A report suggests that 68% of the business data goes unused

Data gathering is a routine process. Organizations are coming up with solutions through data analysis and are predicting the future with gathered data inputs. These commodities have made data an invaluable asset.

Since the Covid-19 pandemic has hit the globe hard, companies have taken the way of remote working. Work from home is an alternative way for organizations to achieve their routine goal without gathering everyone at the office. But all are not in vain. Companies have made data a major player during the lockdown and remote functioning. Technologies like the Internet of Things (IoT), edge computing , and Artificial Intelligence (AI) are widely adopted at a fast pace. However, businesses and organizations need to keep track of where the data is gaining volume.

What is Data Management?

Data management is the process of ingesting, storing, organizing, and maintaining the data collected or created by an organization. The gathered data helps in data analysis, which will further aid operational decision-making and strategic planning by corporate executives, business managers, and other end users. Data management involves a combination of different functions that collectively aim to make sure that the data in corporate systems is accurate, available, and accessible.

The global data management community defines data as 'the development of architectures, policies, practices, and procedures to manage the data lifecycle.' There are five major possibilities of data management, viz.,

• Cloud data management- Integrating data from an organizations cloud application.

• ETL and data integration- Transforming data into a format for high-in depth analysis from the data warehouse. The process involves transformation, summarization and aggregation.

• Master data management- The method of managing critical data in an organization as of customers, accounts and parties.

• Reference data management- Sideline data like postal codes, list of countries, regions and cities that are used on the data field.

• Data analytics and visualization- Performing data analytics and choosing a perfect tool to visualize them in an attractive way.

Data management practices used for analysis and predictions

A report suggests that 68% of the business data goes unused. This is purely because the data assets are not seen on the balance sheet, even though data has the power to drive new sources of revenue and improve customer experiences and operational efficiencies. However, it is obvious that the data can only obtain its value when it is used properly. The data experts and employees are also in the league to ask relevant questions when data analysis is complete. The process can provide predictions and envision the company's future. But it is up to the person asking definitions to analyze how much profit the organizations gain out of it. There are some data management practices used to get data ready for analysis.

Accessing traditional data: More data means more predictors and more solutions. The amount of data plays an important role when it comes to data management. Bigger data are useful when it comes to how the magnitude of data, business analysts, and data scientists can get in their hands. Henceforth, a large amount of data unravels the possibilities of filtering, which data will best suit the analysis. If the current data is less, organizations have to go for traditional data, which will help then make predictions.

Cleansing poor quality data: Large data is important for analysis, so is clean data with good quality. Thousands of messed unstructured data is good for nothing. It is predicted that up to 40% of all strategic processes fail due to poor data. With a data quality platform designed around data management best practices, organizations can incorporate data cleansing right into the data integration flow.

Processing data through metadata layers: A metadata layer promotes collaboration, provides lineage information on the data preparation process, and makes it easier to deploy models. A common metadata layer lets an organization repeat their data processes. This will further unravel data to do better production, more accuracy models, faster cycle times, more flexibility, and transparent data.

Barriers to using data effectively

Data is collected for a purpose. It is not like data can just be stored in a source without being used at any stage. Organizations gather data to improve their services or internal processes. A survey found that the solution to a great deal of data management challenges lies in the mass data operation or DataOps. The discipline of connecting data creators with data consumers is called DataOps. It is revealed that only 10% of organizations report having implemented DataOps fully across enterprises. Most of the organizations admitted that they need DataOps.

Ensuring the secure storage of data

Data, as said, is an asset. Like all other properties being stolen or misused, data has high chances of going that way at the wrong hands. Henceforth, it is up to the organizations to keep it safe and secure.

The data security breach not just threatens a company's stability; it will collapse the whole organization if private consumer data are mishandled. As a result, to ensure the security of data, organizations should take a step forward to educate employees on the importance and value of data. It should ensure that people involved in data processes are aware of the adverse impacts it could bring. Protecting data is a responsibility shared by everyone working under the same roof.

Data management plays a vital role in envisaging companies to collect and analyse data, which will further unravel the possibilities of accurate predictions and decision making. So to surmise, data is important and, at the same time, dangerous if put for malicious use. However, if the organizations take the ideas of data management and leverage it properly, they will see improvements rapidly.