You have done enough research to know that data management is an important first step in processing big data or starting any analysis project. But you are not too proud to admit that you are still confused about the difference between master data management and data management. Or maybe you know these terms with your heart. And you feel like you explained them to your boss or your business units many times.
Either way, we created the bait you were looking for. Print it, post it on the group bulletin board or share it with your mother so that she will understand what you do. And remember, a data management strategy should not focus on one of these areas. You need to consider them all.
What is it? Data is just an asset if you can get it. Data access refers to the ability of an organization to retrieve information from any source. Data access technology, such as database drivers or document converters, makes this step as easy and effective as possible so you can spend time using data – no Just try to find it.
Why is it important? Data that organizations need can exist in multiple sources – spreadsheets, text files, databases, email, business applications, websites, social media feeds, and word data transfers. Iodine. If there is no good way to access data from these sources, collecting information becomes a nightmare. Although it is often a forgotten element in data management, good data access technology is essential for organizations to extract useful data from any type of storage or format mechanism. Any available. Without it, trying to get the data you need is like going to a large, large library with rows of bookstores and being asked to find a specific print without instructions, no maps, no organization and no one to help you.
What is it? When you have access to the data you need, what will you do with it? The most common next step is to combine it with other data to present consistent results. Data integration is the process of defining the steps to do this and data integration tools help you design and automate the steps to do this work. The most popular types of data integration tools are called ETL, short for extracting, converting and downloading and ELT, short for extracting, downloading and converting. Today, data integration is not limited to movements between databases. With the availability of in-memory servers, you can load data straight into memory, bypassing the traditional database completely.
Why is it important? Data integration allows organizations to create mixed data combinations that are ultimately more useful for decision making. For example, a data set may include a list of all customer names and their addresses. Another set of data can be a list of online activities and customer names. In itself, each set of data is relevant and can tell you something important. But when you integrate elements of both datasets, you can start answering questions like, who is my best customer? What is ROUND and Crazy? Next offer? By combining key information from each data set, you can create the best possible customer experience.
What is it? Data quality is the practice of ensuring accurate data and can be used for its intended purpose. Like ISO 9000 quality management in production, data quality should be used at every step of the data management process. This starts from the moment the data is accessed, through different integration points with other data – it even includes points just before the data is published, reported or referenced at one point. to another.
Why is it important? It can easily store data, but what is the value of that data if it is incorrect or unusable? A simple example is a file with the words 123 123 MAIN ST Anytown, AZ 12345, in it. Any computer can store this information and provide it to users – but without help, it can determine that this record is an address, which part of the address is status, or whether the mail sent to that address will be there. Fixing a simple, manual record is not difficult. But just try to implement this process for hundreds, thousands or millions of records! It is much faster to use a data quality solution that can standardize, parse and verify in a consistent, automated way. By doing this at every step, you can eliminate the risk of sending customer mail to an incorrect address.
What is it? Data governance is a framework of people, policies, processes, and technologies that determine how you manage your organizational data. It has a way to ensure your data strategy fits into your business strategy.
Why is it important? Data governance is often driven by the need to comply with state or federal regulations such as Sarbanes Oxley or GDPR. It starts by asking general business questions and developing policies around the answer: How does your organization use your data? What limitations do you have to work around? Who is responsible for the data? How to identify different data types? When you know the answers to these questions, you can define rules to enforce them and develop a data catalog to identify terms. Let’s say you need to determine which data users can access, which data is considered “customers”, which users can change the data (instead of just viewing it) and how to handle exceptions for policy. Data management tools help control and manage those policies, track how they handle and provide reports for audit purposes. And, similar to data quality, you can create a data administration dashboard to help track compliance with these policies.
What is it? Master Data Management (MDM) is a set of processes and technologies that define, unify and manage all common and necessary data for all areas of an organization. This master data is usually managed from a location, often called the master data management center. The center acts as a common access point to publish and share this important data throughout the organization consistently.
Why is it important? Simple: It ensures that different users do not use different versions of the general data organization, necessary data. Without MDM, a customer who buys insurance from an insurance company can continue to receive marketing offers to buy insurance from the same insurance company. This happens, for example, when information is managed by customer relationship databases and marketing databases are interlinked. That leads to two different profiles of the same person – and a confused, uncomfortable customer.
Learn how to trust your data
Despite recent advances in technology and data management tools, many organizations are still overwhelmed by the volume and type of fast-moving data today. With SAS Data Manager, you get data on demand, helping you make decisions that you can trust to run a business based on data. Be ready to act according to what your data tells you – to gain a competitive advantage, solve compliance tasks, increase profits and keep your customers coming back more.