Every organization needs clean and consistent data to thrive. Proper storage and utilization of quality data can provide us with quality insights for business decision making. However, where can you get quality data from? While most of the company data come from internal sources like ERP or CRM software, even more data comes from external sources like the web and other sources. In the internet era, the web is found to be the largest data repository.
As we witness, the overall data volume in the digital universe is growing significantly day by day. There are also no signs of it slowing down in the coming year. Some studies have shown that the data volume is doubling up in size every two years, and the growth rate is like from about 4.4 zettabytes back in 2013 to nearly 44 zettabytes (i.e., 44 trillion GB) by the year 2020. However, an important thing to note about data in 2020 is that most of these are in unstructured formats, which is also highly inconsistent and unorganized. To capitalize on this available data and gain any valuable insight from it, you have to efficiently extract, store, prepare and analyze the data to be consumed at a scale.
Not only should this but the data you acquire also be clean and reliable. To help with it, you need to have a highly reliable platform at the first point. The platform you resort to should treat the random external data with the same control and quality as the internal data sets. Let us walk through some of the strategies to ensure data quality and consistency across the board to enable business decision making effectively.
Further, in this article, we will walk you through some relevant strategies to ensure the consistency and quality of data across the board to benefit your business. To start with, let us discuss some of the background information related to data consistency.
The concept of data consistency
Data consistency means that there should be consistent measurements of variables across the datasets. This may become a concern when the data gets aggregated from various sources. Discrepancies in data from different sources may ultimately create a very inaccurate and unreliable dataset. This will ultimately spoil the purpose of having a database, and any analysis of data becomes unreliable due to its inconsistency. Here we discuss some strategies to make sure that your data remains consistent across high-quality datasets.
Why is data consistency crucial?
Data consistency can distinguish between a great successful business to a failed one. Data is the foundation of success for modern businesses where inconsistent data will lead to ill-informed decision-making. So, enterprises must ensure data consistency, especially while aggregating data from various external and internal data sources. By ensuring the same, you can be more confident in well-informed business decision-making, contributing to business success.
Develop guidelines for the sales teams.
In organizations, sales teams usually have access to a massive amount of data from various unsolicited sources. However, the lack of consistency in handling these data amongst the team members may lead to grave quality issues. So, it becomes critical to ensure that everyone is on the same page when it comes to managing data. If you doubt developing proper database guidelines, there are many providers like RemoteDBA.com offering cost-effective services remote database administration and database management practices.
While most of your data is gathered automatically, the team members who use the database must also be instructed about how to manually input data by ensuring its integrity. For example, they should be given guidelines to follow the data best practices by completing all necessary fields and should always use consistent formats for the name, phone number, and other contact info. They should also try to recognize that some of the information may be more valuable than others. Say, for example, if a prospect’s phone number may be preferable compared to the email ID. So, it is ideal to smartly train the users on how to prioritize their data-gathering attempts.
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The team members should also check the accuracy of data regularly. It is possible that the old data may become outdated from time to time. These dump data may be troublesome to your sales and marketing teams, so these need to be eliminated from time to time to prevent your campaigns from disrupting the analytics. There should be proper practice to review the data consistently and split it into different groups to ensure that each of these groups consists of only accurate and updated data.
It is also very important to have a strategy for data recovery to be used at any point. Accidents may happen anytime, and some of these may end up in major data loss, too, with devastating consequences. Having a solid strategy in place will ensure that the team members know the plan of action for immediately responding to any worst-case scenario to minimize the database damages and avoid any costly downtime. This strategy might also include having a back-up cloud platform support and a set of commands for the employees to practice once the alert is initiated and implementing a formal procedure for reporting and counteracting an accident.
Consolidation of data from disparate source
It is also common that the old systems have to be replaced or updated from time to time. Unfortunately, this may also create some gaps where the old systems may not go well with the new ones, which may compromise the data quality. Here also, database consolidation can be a solution, which can help keep the data cleaner and prevent any chance of overlapping.
Consolidation will let you create a database that houses data from various sources can then mix it well with internal data for easy comparison. In turn, you might also be able to synthesize the data so that it becomes easy to digest it and maintain an advanced level of homogeneity. Using a standardized OS is the first critical step towards data consolidation. You may pick your single right platform and make sure that all the software and applications used are supported.
You should also test your workload to ensure compatibility with the unified database and operating systems choices you make. Ensure your hardware infrastructure is also able to handle the workload of the consolidated database as you plan. These considerations also encompass the requirements of I/O storage, processing, and memory capacities, too, among others.