Data Management Best Practices to Optimize your Database Performance
If you are into business, you may surely have various important data related to your company operations. You will have various types of important information coming from various crucial sources, both internal and external. Most of the time, what you may lack is a good data management practice, which will help you get all this information together to have a closer look at it and get some insights.
One can only get to know about these practices by becoming an expert in data management and visualization tools like Power BI. There are some helpful online training programs such as the Power BI course which can help you achieve expertise. Doing this will get you many new opportunities like taking your business to the new markets or bring in some profit beyond your expectations. But how and where all your data and information systems are uses for your business to be positioned?
Will you be able to access these when you need those the most? Do you know whether the data is accurate and clean to serve your purpose? Will you be able to easily tie all these data together from various sources no matter what format it is in and how frequently it changes?
All these big questions are out there when you are thinking of setting up a database and using it for your enterprise management. Your data must be ready to support business analytics and decision-making, but frequently, this is not the truth in front of you. To make your data-centered analytical journey enjoyable, you should be able to use the data well. This is data management.
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Data management is a very hot topic of discussion among enterprise decision makers and data scientists and data analysts. There are plenty of resources, services, and software applications available out there for enterprise data management and analytics. However, you need to be very careful while dealing with these data management practices.
The underlying data should be cleansed and up to date to make your analytics process successful. Further in this article, we will discuss the best data management practices to follow to ensure that your data stored is good enough to run your analytical procedures effectively and in a result-oriented manner.
Data management best practices
It is a fact that many companies are doing data analytics on their enterprise data, which is not prepared for analytics. This data may be incomplete or not clean. Maybe the organizational infrastructure may not accommodate new data formats like unstructured data or simply text or mail messages.
Sometimes, the case may be that they are working on duplicate, outdated, or corrupt data. Until all these organizations find a better way to effectively manage their data, their analytics results may not take them anywhere. So, how difficult is it to manage and filter data and get it ready for analytics?
Most of the companies spend 80% of their data model development time only for data preparation. You should know how to prepare this for analytics as well, for which you may take the assistance of reliable database service providers like RemoteDBA.com.
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Data management best practices to prepare your data for analytics
Simplified access to traditional and emerging data
More data means better predictions. So, it is better when it comes to how much data your business analysts or data scientist may get to their hands to work on. By gaining access to more diversified data, it becomes quicker and easier to determine which data can predict the best outcome. Software like SAS will help you in this by offering an abundance of data access, making it easier for the analysts to work with an ever-ending flow of data from ever-increasing sources, structures, and formats.
Strengthen your data analytics arsenal with advanced techniques
Many applications are offering highly sophisticated statistical analytical models inside the ETL flow. You can do activities like frequency analysis and identify the missing values and outliers. These will help you have the other measures, like average, mean, median, mode, etc.
Summary statistics will help analysts understand the variance and distribution as data is not always normally distributed. The correlation will show which variables or combinations may be the most useful based on the predictive capabilities.
Scrub the data to build better quality
Up to about 40 % of all the strategic procedures tend to fail due to poor data inputs. With data quality platforms designed around better data management practices, you will incorporate data cleansing into your data integration flow. Pushing the processing down to the database level will help improve the overall performance.
It will also help to remove invalid and unclean data based on the analytical methods you are using. You can also enrich data through a grouping of data that was originally in smaller intervals.
Shape your data using flexible techniques for data manipulation
Preparing your data for analytics may require transforming, merging, normalizing, and aggregating your data sources from various tables into one wide table called an analytic table. This will help simplify the direct data transmission with graphical interfaces and intuitive transformations. It will also let you use other data transmissions like appending data, frequency analysis, partitioning, and combining data through various summarization techniques.
Sharing metadata across analytical and data management domains
Another common method in data layering will let you consistently repeat your data preparation procedures. It will also help better collaboration and provide lineage information on your enterprise data preparation procedures. This will make the process easier to deploy various data and analytical models. You can also notice better productivity, more accurate data models, faster data cycle timings, and auditable, transparent data through this procedure.
As we know, data is the foundation of any data-based decision making and analytics is one of the hottest topics of discussion these days. It is undeniably a very challenging technology, but as you enter into the magic world of analytics, you need to remember that analytics’s underlying resource is data. So, do not underestimate how important data is for your business. Be very careful while adopting ways to gather data, store data, manipulate data, and utilize it for your analytical purposes.
There are plenty of tools and technologies like software and hardware applications out there for data collection, processing, data manipulation, and analytics. You need to understand your enterprise requirements well to choose the most appropriate data management tools and applications to serve you the best. You may also take the assistance of expert data analysts and data scientists to make a plan for it and execute your data management procedures.
Data Management Best Practices to Optimize your Database Performance