All modern-day companies, no matter how large or small, have to reckon data management. Data management is how one collects, keeps, and uses data securely, efficiently, and cost-effectively. The main goal of data management is to help people and organizations better use data legally so that they can make strategic decisions and take actions that maximize the benefit to the organization. It is vital to have a properly structured social intelligence program that utilizes robust, accurate data. A well thought out data management strategy is more critical than ever as businesses increasingly rely on virtual or intangible assets to create value.
However, data isn’t just customer records and other externally sourced information- it can be any data. Employee records, credit card information, sales leads, marketing activity, website content, social media interactions, are just some of the pieces that fall under manageable data.
It takes a lot of hard work to turn data into something usable. Brands and agencies today are getting their data from countless different sources and managing them on various platforms. However, without proper management, you can end up with duplicate records, incorrect information, wasted time, and storage space. This also leads to difficulty assessing the accuracy of social listening campaigns, among other issues that come with poor organization.
Enter Data Management
Data management’s main objective is to keep information organized in a way that is both practical and usable. At its most fundamental level, data management works to ensure that an organization’s entire body of data is accurate and consistent, readily accessible and adequately secured.
Besides being a way to eliminate duplicates and standardize formats, data management also lays the groundwork for data analytics. Without proper data management, analysis is impossible and likely unreliable.
Data Management Best Practices
Addressing data management challenges requires a comprehensive, well-thought-out set of best practices. Although specific best practices vary depending on the type of data involved and the industry, according to Leadspace, the following best practices address the primary data management challenges organizations face today:
Outline Business Goals
It would be best if you started small when it comes to data management. Begin by outlining what your goals are with your company’s data. Do you have any social intelligence goals to meet? How are you planning on using your data to serve your customers better? Can you combine any data sources to get deeper insights? Knowing what you plan to do with the data you collect can help you keep only the information relevant to your purpose. This ensures that your data management software doesn’t get overcrowded and unorganized. Too many companies keep and continue to store way too much data for which they have no use. By keeping only the data that your company is going to use, you’re helping to keep your data management software clean and under control.
A few example goals your business might have are:
- Improving decision-making
- Creating or enhancing automation and processes
- Audience targeting / creating buyer profiles to harness social listening technology
- Finding customer buying habits and patterns
- Training sales and marketing teams on data use
You can do countless things with data, but it’s imperative to start by outlining your goals. Your business data goals will direct your data management processes, so you don’t end up with heaps of information irrelevant to your company’s goals.
Prioritize Data Protection and Security
Security is an essential step to ensure your company doesn’t fall victim to a data breach which could endanger the information of your entire customer base. Consumers are generally quite unhappy when unknown sources gain access to their data, as we’ve seen many large company slip-ups. Data protection and security need to be the number one priority for your business’s data management.
The General Data Protection Regulations (GDPR) rolled out in the EU last year, making it more difficult for companies to use customer data. Your company must follow all applicable guidelines to ensure your leads and customers’ privacy. This includes respecting unsubscribe requests on email marketing campaigns, among other actions. Additionally, GDPR impacts not only businesses that operate within the EU but all companies that market and sell to customers who reside within the EU.
Proper data management software can help ensure the safety and security of your data. Complying with GDPR and other regulations when it comes to collecting data can also improve your data protection.
Focus on Data Quality
Limiting your data to only the necessary information your company needs to meet its goals is a great way to improve data quality. The data your company is collecting must remain clean and reliable.
First, data should be regularly checked for accuracy. Old data can become outdated and irrelevant to your sales and marketing teams quickly. Real-time data such as that used in social listening programs can take up space, so periodically cleaning and checking is essential. Obsolete or stale data should be purged from your data management software often. This process keeps data from negatively impacting your automation, analytics, and other processes within your sales and marketing departments.
To help your team focus on data quality, train all team members to collect and input data. Some input may be automated, but for instances where users manually add data to your CRM or data management software, training is necessary. This precaution keeps data from being input incorrectly, thus preventing problems down the line.
Ensure the data is checked and cleaned before it is used in any analytics or reporting to improve the accuracy of all metrics. Making data quality a top priority helps keep all aspects of your company’s data use clean and reliable.
Reduce Duplicate Data
There are many ways that your company could receive duplicate data from a lead or customer. Your brand should have processes in place to handle potential redundancies. For example, a lead may be interacting with multiple lead sources or offers or returning to make another purchase. It is important to ensure your company has processes in place to avoid adding duplicate data to your data management system.
Although many companies may not think about this when first setting up their data management, it’s essential to create systems that allow data to be updated or changed when someone opts in more than once or makes a return purchase. Putting precautions when it comes to duplicate data and redundancies is another way to ensure your data stays clean.
Combine Sources When Possible
One of the biggest roadblocks in managing data is the spread of information across multiple platforms and databases. Social listening data may go into one bucket while website data and consumer information may go elsewhere.
You must ask yourself if you are integrating all your sources where you can. For example, consider connecting your CRM data with your social listening data. This integration will easily enrich records and enhance intelligent decision making. Synthesio’s Social Listening Platform’s API brings advanced social analytics directly where your data already lives, making your research process more manageable.
Prioritizing Data Management
We cannot emphasize enough how important data management is. Clean data makes your sales, marketing, and leadership team’s jobs easier while providing them with insights and information that could be crucial for connecting with customers. With all the data that exists out there, you want to make sure you’re using it to its full potential. Optimizing and prioritizing data management makes a huge difference when harnessing social intelligence and getting the most out of your marketing technology spend.
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