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Data Quality in Business Intelligence: Why it is so important, and how to secure it?

Data Quality in Business Intelligence

Table of Contents

Business owners lose a lot of money investing in BI tools and data analytics. So make sure these investments are commensurate with the risks your business may face. And an important note that you need to know is that an investment in BI tools will not provide a good return on investment if the Data Quality In Business Intelligence matters. Principles and decisions made from poor data quality can seriously and negatively affect your company.

What is Data Quality In Business Intelligence?

Data Quality in Business Intelligence

The condition of your Data Quality In Business Intelligence is measured by characteristics such as correctness, consistency (in all fields across data sources), integrity (if the fields are complete), and usability. A perfect score in all of these areas implies high-quality data, which is ideal for processing and analysis.

One of your company’s most valuable resources is data. Poor data quality practices cost businesses up to 20% of their revenue (Kissmetrics). Your business intelligence is only good when it is fed with quality data. Check to see whether your data analysis is losing value because the data it is fed is erroneous.

As data-driven businesses increasingly rely on insights, customer databases, and marketing efforts to drive vital business decisions, high-quality data has become critical. Inconsistent or incomplete data can erode customer trust and undermine your market position.

How Does Poor Data Affect Business Intelligence (BI)?

Business intelligence adheres to the core computational principle of Garbage In, Garbage Out (GIGO). Essentially, the quality of the input data dictates the quality of the output. Inaccurate or incomplete data might have the same influence on output Data Quality In Business Intelligence as invalid or obsolete data.

Your business intelligence objectives may be hampered by poor data because it reduces decision-makers’ confidence. Poor data contributes to making bad judgments, which could be detrimental to your organization.

For example, a business chooses to launch a product using BI technology. Understanding the market’s most potential demographics, conducting a competitive analysis of the market, and using customer surveys to identify market gaps are some of the typical ways campaigns can be highly effective.

The Data Quality Reports used in this campaign will greatly influence data collection. Poor survey question selection or inappropriate interpretation of competitive analysis can lead to misrepresentation, which will have an immediate impact on campaign results.

This can be a bug that costs thousands to hundreds of thousands of dollars, depending on your industry.

Major causes of poor data quality

Many businesses are attempting to set up processes to clear up bad data. However, just as important as cleansing the database is preventing bad data from entering it in the first place. You can avoid bad data by figuring out what causes it. Here are several main reasons why Data Quality In Business Intelligence issues occur:

Manual entry

Despite the fact that there are many automated alternatives, organizations still enter data into their systems manually. It takes a lot of staff to manually enter data into the database. However, this option also has a lot of opportunities for incorrect data to be entered into the system.

Acquisition and mergers

A lot of data is involved in acquiring and merging a company with your business in addition to financial or business considerations. The format of data entered into your company’s database is determined by their policy, not yours. The Data Quality Analysis of your will be affected by incorrect data, duplicates, and inappropriate formatting.

Real-time updates

Real-time data collection and knowledge acquisition are quick processes. Data Quality In Business Intelligence is automatically and instantly updated in real-time in the data servers to assist you in making effective and current decisions. However, due to the time limit, there is a danger of obtaining inaccurate or falsified data.

Indiscriminate data collection

Many organizations gather all the information they can, but not all of it is relevant to their operations. Avoid gathering unnecessary data and instead find and save the important info. Large amounts of useless data will start to accumulate, taking up unnecessary storage space and becoming time-consuming to filter through.

System upgrades

Poor data can also result from often updating the hardware or software on your systems. There is a good probability that data will be lost or corrupted during system upgrades.

Impact of Data Quality In Business Intelligence on data analysis

You can gain various advantages from high-Data Quality In Business Intelligence and analysis, including the following:

  • Your data will be more dependable in terms of quality and consistency if you use a data management system.
  • You can make decisions with confidence when you have access to high-Business Data Quality, which also helps you achieve your goals.
  • By determining the target audience, you can raise the caliber of your marketing initiatives using reliable data.
  • Good Data Quality In Business Intelligence helps you understand client wants and enhances the caliber of your goods and services.
  • You have an advantage over your rivals who lack high-quality data because you have it.
  • By recommending more closely related products to customers, you may anticipate their buying habits and boost business growth.
  • You may visualize new market trends and base important decisions on them by connecting your Data Quality In Business Intelligence solutions.
  • You can cut the costs associated with handling low-quality data by maintaining Data Quality for Business Intelligence.
  • Using a data quality management system, you may identify and address your organization’s deficiencies as well as track its growth.

How Do You Measure Data Quality In Business Intelligence?

Data Quality in Business Intelligence

Regularly evaluate your Data Quality In Business Intelligence to ensure that it is serving you to the fullest extent possible. Below are 6 points of reference for data quality management listed in a Gartner study:

  • Consistency: Do values remain the same when a single piece of data is saved in different places?
  • Accuracy: Does the data accurately reflect the characteristics of the modeled object?
  • Relevance: Do the data make sense in light of the goal?
  • Existence: Does the company possess the appropriate data?
  • Integrity: How precise are the connections between the various data sets and data elements?
  • Validity: Do the values meet the criteria?

The majority of businesses use software to find and fix mistakes. Data quality technologies, which guarantee that an organization’s Data Quality In Business Intelligence is accurate and valuable, are widely available. With features like program management, responsibilities, use cases, and processes (such as those for monitoring, correcting, and reporting data quality) as well as building up organizational structures, many applications offer more than just data assessment.

For example, Informatica Data Quality, Microsoft Data Quality Services, Ataccama ONE, and Oracle Enterprise Data Quality are a few of the most well-liked products on the market right now.

The good news is that your data can be repaired if it is damaged. The following 4 phases can be used to develop a plan to overcome problems with Data Quality In Business Intelligence:

  1. Provide executives and company divisions with the resources they need to create successful quality standards for data management.
  2. Analyze the worth of the data. Be proactive and assess the information assets you have as well as the expense of using low-quality data. For optimum success, these figures can be closely correlated with important business measures.
  3. Make precise time estimates for the implementation of data quality software. The fact that so many businesses underestimate this period of time breeds mistrust between business operations and IT.
  4. Make the most of the tools you have for Data Quality In Business Intelligence. Because this kind of software is expensive, keep your standards high while remaining adaptable and delivering value.

Data quality management

Data quality management refers to the methods and techniques used to ensure that information is consistently of high quality. It’s also about discovering shoddy data, cleaning it up, and making it useful to your business intelligence systems. With the right data quality management system in place, you can use Business Data Quality to better understand your business and use that understanding to help your organization grow.

Data Quality in Business Intelligence

Why is data quality control required?

Since information enters an organization in a variety of ways, not all of it is accurate and flawless. It might be redundant, out-of-date, or inconsistent. You cannot use it to make significant judgments if the information is not accurate and consistent.

You could lose a lot of money if you make business decisions on inaccurate and untrustworthy information. The use of data quality management enables you to identify low-quality data and determine how it enters your database. Once that data has been cleaned, you can stop additional from entering your database.

Roles that ensure data quality

To avoid a data crisis in your firm, you need a solid data management strategy. Data management is made up, first and foremost, of the team that will be stewarding operations.

Data Quality in Business Intelligence

Data owner: The organization’s data are the sole responsibility of the data owner. They are able to share, create, edit, alter, and limit access to the data.

Data steward: The management of the data sources and the data governance process are within the purview of the data steward. They assist in identifying issues with the data sets and offer suggestions for risk mitigation and Data Quality In Business Intelligence assurance measures.

Data manager: A data manager is in charge of efficient data management, setting up policies and practices to handle vast amounts of data, and controlling the data as it enters the business.

Data users: One who enters data on a regular basis is referred to as a data user. They enhance the quality of data entry and ensure that there is a minimum human mistake by abiding by the organization’s guidelines.

How to maintain Data Quality In Business Intelligence?

Maintaining Data Quality In Business Intelligence is just as critical as cleaning and processing the data. You can guarantee the accuracy of your data by using these practices in your company.

Collecting data

You can guarantee the caliber of the data entering your databases by using the right data collection strategy. By classifying data according to your company’s requirements, you can keep departments from being confused and guarantee that they only gather the data they require.

Ensure standards

You can distinguish between desired and undesirable data by developing Data Quality In Business Intelligence standards. The effectiveness of your data analysis and presentation will increase as a result.

Correcting data

Giving your team clear instructions and procedures for data correction helps them comprehend the data they must process and assures the accuracy of the data.

How To Improve Data Quality for Business Intelligence?

You cannot solely rely on data to guide your company decisions. Data quantity is crucial, but so is data quality. You may do great things in your business with good data. You should determine if the data meets the following criteria to make sure you are using high-quality information.

Essential factors for clean data

You cannot solely rely on data to guide your company decisions. Data quantity is crucial, but so is data quality. You may do great things in your business with good data. You should determine if the data meets the following criteria to make sure you are using high-quality information.

Completeness

Having all the information necessary for your intended use is what is meant by completeness. For instance, if a customer’s name and contact information is required but their address is not, you can still consider your customer data complete without it.

Consistency

Data accuracy refers to whether the compilation is free of data manipulation or duplication. There is frequently more erroneous data there when there is a greater volume of data. Always make sure the info is correct and trustworthy. The quality of the data will improve as a result.

Timeliness

Timeliness refers to the data’s readiness to use when required. There is no use for the data if it cannot be accessed when required. If it changes, it should be immediately updated.

Integrity

Your database’s data should be legitimately connected to the outside world. There is a possibility that the data on your server is duplicated or inaccurate. Consider the case when you have a customer database for product sales. If a customer name is lacking for a particular product sold, the data is then invalid and useless.

Poor Data Quality can be a significant drain on company productivity. According to MIT Sloan research, employees spend up to 50% of their time improving the quality of their data. This could be due to a variety of factors such as faulty data or inconsistencies across multiple sources.

The Data Quality In Business Intelligence obtained so poor can not only reduce working efficiency but also contribute to output inaccuracies. This is especially true for business intelligence jobs where the accuracy and Data Quality In Business Intelligence supplied is critical.

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