If we analyze the success stories of business leaders and organizations it’ll come to our notice that they’ve had always relied on some form of data to help them make business decisions. Data collection previously used to involve manual data collection such as talking with customers face-to-face or taking surveys via phone, mail, or in-person. Whatever the method, most data had to be collected manually for businesses to understand their customers and market better. Because of the financial cost, time, difficulty of execution, and more associated with data collection, many companies operated with limited data.
However, today this scenario has changed a lot. Gathering data to help business better understand the customers and markets is easy. Almost every modern business platform or tool can deliver thousands of data for your business to use.
Similarly, organizations nowadays are continuously looking to adopt the new kinds of coming technologies, like automation, artificial intelligence(AI) and the Internet of Things(IoT). But the success of organizations in doing so and their ability to differentiate themselves in their respective markets will be totally dependent upon their ability to get data management right.
As it’s rightly said by W. Edwards Demming –
“Without Data, you’re just another person with an opinion.”
Also, just having the metrics isn’t enough. Firstly, you’ve to make sure that the data you’re collecting is relevant to your organization’s goals. It should indisputably report all pertinent information – positive or negative. Then, the metrics collected need to actionable.
However good-quality data has several beneficial impacts on organizations like:
The better the data quality, the more confidence the team using that data will have in the outputs they produce, thus lowering risk in the outcomes and increasing efficiency. The importance of data in decision lies in consistency and continual growth. It enables companies to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights.
With the help of data companies, decision makers are now able to make better-informed choices than in the past by shaping and filtering the data their organizations have collected. Today, the top companies around the world use data to make decisions about their business. When better decisions are made, businesses experience benefits which extend beyond the expected margin.
Good-quality data allows your sales and marketing teams to be more productive. Instead of spending time validating and fixing data errors, they can focus on their core mission of building an audience and creating sales opportunities.
If properly accessed and applied, data can show firms the road to increased productivity in the workplace. Tools and resources are available in larger quantities than any company could ever hope to employ, so there’s no excuse for leaving data out of your processes.
Today’s marketing is about 80% based on the quality of data and I say this as one of the responsible B2B Marketer. Data quality is important because, without high-quality data, you cannot understand or stay in contact with your customers and prospects
The importance of data quality is also evident in marketing efforts. In the past, the lack of demographic and other important data about customers meant that companies could only market to the broadest audience, thus wasting money on targeting people who were unlikely ever to be interested in the particular good or service being offered.
However today the wealth of quality data in regards to the demographic information of audience available enables marketing to more tightly focused and more likely to achieve the desired results with minimum wastage of resources.
Data that is up to mark and valid, helps you understand and improve business processes so you can reduce wasted money and time. The quality data allows a business to make informed decisions in the day-to-day business processes by presenting data in a way that can be interpreted by management.
Customer is King! and it’s important for every business to maintain a healthy relationship with the customers for future sales and growth of the business.
It definitely makes sense that accurate data will improve relations with your customers. Data enables you to truly know your constituents, which keeps you from sending mail that they do not want to read and helps you to anticipate and meet their needs. Both of these things create a great deal of goodwill with your customer base and are more reasons why data quality is important.
Today’s marketing environment is very broad and keeps constantly changing. A product that has been launched only a week ago has to face a challenge from other company’s upgraded product within a week. The tastes/choices and buying process of the audience nowadays are constantly changing and this is creating a big challenge for the organizations.
Knowing your customer is key for any business endeavor. Successful businesses understand what their customers want and the most effective way of making their product or service available to the end user. The clearer you see your consumers, the easier it is to reach them. For the same, it is very important that the organizations are provided with quality and updated data to study the ongoing market scenarios and behavior of the buyers.
Poor data can lead to lost revenue in many ways — communications that fail to convert to sales because the underlying customer data is incorrect. Poor quality data results in loss of customer confidence, corporate liability and the loss of current and future business.
This is the reason why we B2B Marketers should focus more on the practice of limiting the collection of personal information to that which is directly relevant and necessary to accomplish a specified purpose. and make sure that the collected data is accurate by all means.
In fact, the European Union has recently included this in new laws of the Data Protection Act that will come into effect soon. The act says, “Personal data shall be adequate, relevant and not excessive in relation to the purpose or purposes for which they are processed.”
Data maintenance and quality assurance is an ongoing and often heavy process in the day-to-day lives of respective authorities handling the data. The leaders that position their organizations to manage data correctly and understand its inherent value will always have the advantage over their competitors.
Maintaining data quality involves periodic reviews and scrubbing—or cleaning—the data. Scrubbing includes updating, standardizing, and de-duping information, as well as making sure all appropriate data is present. The quicker data can be cleaned, culled, and unified, the quicker insights can be drawn from it.
At Apprise Marketing Services – AMS, we don’t take data quality for granted. It’s an integral part of the lead generation process. The fact is that lists get stale quickly. Out-of-date contact and company information significantly weakens promotional activities. Quality leads move from position to position. Whether it’s a title change, company change, or email change (or all three), you need to clean data to make successful sales.
Our data mining team knows what is genuine and what is a hoax. Once you assign a task to them they will give you the best contacts for your business activities.
I’d conclude with a famous quote from Mr. Tim O’Reilly depicting the impact of data in our lives.
“We’re entering a new world in which Data may be more important than software.”
Lastly, but most importantly, data quality can often cover not only the cleanliness of the data in the system but also the governance of that data. Compliance and privacy are at the forefront of data quality today, with increasing pressure from consumers and government to protect sensitive data. The emergence of new regulations such as the General Data Protection Regulation (GDPR) Act governing the use of data related to individuals has added pressure on organizations to safeguard the information of individuals and to be more accountable when it comes to how the data is used and by whom.
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