Data Cleansing Tools: Improving Analytics and Business Intelligence with Clean Data

Data Cleansing Tools: Improving Analytics and Business Intelligence with Clean Data

 

To understand and implement trends that enhance business performance, it's essential to make use of relevant data, placing together valuable information for business intelligence reporting. The point is, do you have clean and accurate data which is the foundation of any business that wants to be data-driven. If you’re not using a data cleansing tool in your data warehouse or your Master Data Management (MDM) system, you’re likely basing important decisions on faulty data.

 

According to the Harvard Business Review, bad data costs firms around 3.1 trillion [FK1]  every year. The reason behind why bad data is so costly to organizations is because the management underestimates bad data and does not make data quality a priority. Given that it’s both expensive and time-consuming, most firms opt to fix data to some extent on their own, which leaves a significant amount of data undiscovered. As a result, bad data makes its place across systems, and is reflected in reports,  transactions, customer experience and business decisions. Very few concerned organizations put forward the efforts to fix the data at its origin, by reaching out to the people responsible for it.

 

What it is and how it works for Business Intelligence?

Investment in business intelligence and analytics demands dedication to data quality. Successful BI and analytics team always emphasize on 3 things:

1. Healthy data
2. Effective data integration
3. Real-time data hygiene

 

The first step in data quality and achieving healthy data is to implement a data cleansing process to identify and fix inaccurate, incomplete, and irreverent data.

 

More difficult than one might expect though. Those new to information cleaning regularly utilize the essential "detect and remove" or regex works [FK2] through editors or spreadsheets, or utilize in-house algorithms. At Data Ladder, we've seen that in-house systems ordinarily incorporate single open algorithms[FK3] , and offer a heavy yet efficient methodology. That means more accuracy and less time-consuming.

Smart, future-oriented businesses that are serious about business intelligence prefer using a data cleansing tool for such purpose. They realize it’s not just about incorporating algorithms  it’s more about the entire process flow, how well the process is executed and managed end-to-end, how well data sources are identified and integrated, how different parts of the system join together to give a meaningful output, how different algorithms work togetheretc. Ideally, your data cleansing tool should be monitoring your data to prevent any future instances of bad data.

The cleaner your data flow, the better your analytics and by and overall business insight.

Questions You Need to Ask When Cleaning Data for Better BI

Ensure more meaningful outcomes for your business intelligence initiatives by asking these 5 questions before you start prepping up your data cleansing tool: 

Where does the required data live and how hard will it be to extract it?

This usually depends on your technological infrastructure. Usually, enterprises use 65+ separate data sources. [FK4] The data you need for analytics could be stored in Big Data lakes, spreadsheets, SQL databases, social media, CRMs, etc. For instance, if you are focusing on customer data for analytics, make sure your CRM data cleansing tool can integrate with your organization’s Salesforce and clean it efficiently, or whichever CRM you’re using.

How will this data be gathered or imported into your data cleansing process?

 Will the information be manually downloaded from your source frameworks and afterward uploaded for cleansing by existing faculty? On the off chance that your information purging device bolsters group loads, you can import the data manually and afterward plan occasional imports routinely. Then again, you can incorporate an API for continuous information import and cleansing.

What sources provide the most accurate or reliable data?

 

 

The same type of data may reside across different data sources in your organization. Which one do you choose? Entity resolution is a good option here, so you can match across your data sources and get a complete record of each entity.

What method will be used to ensure data stays clean?

How many individuals are approving new information as it comes in? Will the framework remain strong when it experiences unwanted data? API solutions are again a decent alternative here, helping you set up a data quality firewall behind web structures, and so forth so messy information is detected and fixed as it makes its way into the system.

 What will be the source of truth for your data? 

On the off chance that your reports are utilizing information from both internal and external sources, or regardless of whether it's rolling in from a wide range of sources, how would you accommodate them? Coordinating your information to make a Single Source of Truth and afterward utilizing that for BI is strongly recommended.

 

Why Your BI Efforts Will Fail without Clean, Accurate Data

Data cleaning is known as a key element in data science basics, as it plays a vital role in the analytical process and helping uncover reliable answers.

Quite frequently, business leaders resort to putting the horse before the cart. As in, dumping data researchers into the condition in their race to accomplish advanced change. They neglect to understand that these data researchers will even now need to invest most of their energy cleaning the information, as appeared in the pie graph at the top.

With the correct methodology, organizations can better position themselves from improved evaluation — without including costly data scientists.