Data Management

The name only means that managing the data overall. It is the practice of collecting keeping and using the data efficiently. There are many big organizations which are making use of data for getting informed business decisions and deep insights into various consumer aspects. It is also referred to as knowledge management and can help achieve business its growth outcomes by a well-defined data management strategy which includes data management technologies needs and regulatory requirements.

Benefits of Data Management

• Assists in handling a large amount of data and gathering insights
• Follows a more focused approach towards data-driven marketing and burgeons customer loyalty
• Ensures data secrecy and security
• Stores data across multiple cloud platforms and assists in developing various applications
• Sustains business growth and improves decision making and reporting

Difficulties in Data Management

• Getting hold of data management is not that easy as every time new changes can be seen and according to that data has to be modified
• At each and every step, data should be carefully analyzed and evaluated as the data is stored all the time so the organizations need to monitor the type of questions in the database and change the indexes
• Gathering a lot of data will not work as sometimes people have such a huge amount of various types of data with them but they do not know how to effectively use it
• In organizations, data is stored in data warehouses and unstructured data lakes but the data scientists should know when and how to transform that data into the required model or shape
• Better regulation and compliance controls resulting in greater collaboration and revenue growth

What is Integrated Data Management?

A tool which helps facilitates data management and improves the overall performance. It provides contextual data-driven solutions and overall improving data accessibility. It defines business policies and manages various upgrading of applications and also has the capability of simplifying solutions.

Before understanding data management you must be well versed with few terminologies:

• Data Governance – It is a data management concept which assists in ensuring the formal management of data assets
• Data Streaming – It is generating data from different sources. Analyzing the data by applying various logical implications, recognizing the trend in data sets and filtering it for multi user. It takes the assistance of big data as well. The data is gathered from various sources and then send in a small piece(KBS). Data streaming includes a wide variety of files such as mobile applications, social network information and a lot of others. The information has to be analyzed as it gives the various business an edge in dealing with their competitors
• Data Federation – It allows you to store and combine your data gathered from various sources without having to store the data in a new location. It is an alternative model for storing the data. It creates a virtual database into middleware that is used to present data through a diversified architecture. Data federation technology is also known as data virtualization technology. Data federation technologies allow you to store central data and link it to one or more remote sources. The central store allows the consuming application to query the linked as well as data stored as when the query is received, the central data source sends subqueries to the linked data source in order to extract the data and then combines the final output before presenting it to the ultimate user
• Data Integration – It allows you to integrate the varied type of data for the ease of carrying out the data management process. It combines the data from various sources and gives it to the user in a single database form to use. It commences with the ingestion process but rather also include steps such as cleansing, mapping, transformation with the help of data integration numerous data volume can be merged into one effective single output overall encouraging collaboration between external and internal users. Data integration systems are also defined as(G, S, M) where G is the global schemas, S is a heterogeneous source of schemas and M is mapping the queries between heterogeneous source and global schemas. There are two models corresponding it which are Global as view or GAV and Local as view or LAV. Data integration takes the assistance of various analytic stools to produce effective data-driven results. It simply uses business intelligence by creating data warehouses and data lakes. Overall it saves time, reduces the errors and delivers valuable data
• Data Access – With the advent of technology, now there is no need to search out the data as with the coming of numerous data related software information can be obtained in just a couple of minutes making it widely accessible to many people all around the world. It refers to users ability to retrieve the data and then the ultimate user can store it or make it accessible according to the convenience. Data can be accessed in two ways-Random accessing of data and Sequential accessing of data. Under the sequential access, each segment of data has to be read until new or other data is found. Sequential data allows the data to move until the next data is accessed in appropriate time. In random access to data, data is usually split into parts and experiments say that sequential data is much faster to load as it requires s fewer operations.
• Data Architecture – It includes a complete analysis of the relationships of the organization technologies and data types are composed of data, policies, rules and how it is stored and put to effective use in various data systems. Data integration is dependent on data architecture as certain rules, policies need to be defined. It tells how the data is processed making it easier to design data flows and control the flow of data. There are three architectural stages and they are conceptual which represents all business entities, logical which show how the entities are related and the last one physical which showcase the realization of mechanisms

How to make your Data management better?

There are many challenges while handling data but with proper management of data, the results can be more effective. Let’s see:

• Utilization of technology – By taking the assistant of machine learning and artificial intelligence to monitor database queries and optimize indexes. Also, it saves the time of data scientists from time-consuming tasks
• Develop a data science environment – It automates the work to as much extent as possible leaving behind the manual work of transformation and resulting testing of new models
• Create a discovery layer – Creating a discovery layer on top of your organization’s data tier makes the workload lesser for data analysts and sciences as they can search the data sets and effetely utilize the data

Best database management software

• SQL server by Microsoft – Database management system developed by Microsoft as a database server. It delivers the next generation of scalable e-commerce and line of business solutions.
• My SQL by MySQL – It includes an advanced set of features, management tools and technical support to achieve the highest level of MySQL scalability. It also reduces the risk and cost of developing MySQL applications.
• Oracle database – A leading relational database that offers secure data management and transaction processing. It has features like data migration, database conversion, data replication and it can run on all platforms
• Amazon simple DB – A flexible non-relational data store that offloads the work of database administration and the best thing about it is that is NoSQL based. The ease of access and scalability are some of its great features. Moreover, it is developer friendly with great documentation and REST API
• IBM Db2 – It can run on any operating system and Db2 can be used with XML data too. The retroversion of data from Db2 is very fast making it one of its likeable feature. Data can be accessed and stored from different locations. It can use either common line interface or GUI to access and manipulate databases. It can also be connected to other tools like DBVisualizer for viewing charts and tables
• SAP HANA – The best multi combination platform that is driven by the data platform as a report generation tool. It is a versatile solution for many businesses as it has memory computing technology that provides an in-memory database for fast data access and processing to users. Some of its additional operations are transforming data management, process data in memory, advanced analytics and can develop applications.

Data management though have a very important part to play in various organizations has also been ignored by many people. Data management practices and technique, if applied successfully, can help in generating a greater amount of revenues for the business as data management has a lot to provide with efficacy and secrecy of various types of data and that data can turn out to be a blessing for organizations if they find out how to use it correctly. There are many other software and tools available in the market other than that I mentioned above. So if a person does not have any knowledge in the field of data management, then the person can take some beginner level courses from various online sites to start off a career in data management and can lay the strong foundations of data management.