10 Data Management Best Practices for SMBs

The goal of data management is simple: make sure your business has access to the right information, at the right time, in the right format. But it can be challenging to keep track of all your data and meet that objective. To help you get the most out of your data without putting too much stress on IT resources or slowing down your workflow, we’ve put together these seven best practices for managing all types of business information.

Benefits of Strategic Data Management

Your business can significantly benefit from organized data. Consider the different types of data – marketing, sales, operational, financial, supply chain, etc. The list goes on. Much like how all these departments work together, so should their data.

Think of just the marketing and sales potential of better product and customer data:

  • Communicate with customers better by knowing who they are, how they can be contacted, and what they’re interested in buying
  • Personalize products or services based on individual preferences
  • Understand customer needs so that you can target marketing campaigns more effectively

Or you can infuse agility into your operations with a better understanding of how the supply chain is affecting your production line. You can anticipate problems and create alternative workflows to move around these challenges with agility, keeping you ahead of the competition.

Data has the power to make your business more powerful and successful. To tap this power, you need to implement and maintain strategic data management.

1. Define your SMB’s Data Objectives

The first step to strong data management for your business is defining why you need your data, your use cases, and your goals for it. Company data varies in both type and purpose. Consider, for example:

  • What’s your goal for data in the future, and what do you want to accomplish?
  • How will you use it? For example, are you using your data to improve customer experience or drive revenue growth?
  • What types of data would be most useful in achieving these goals?

2. Collect the Right Data

Most businesses have too much data and need to narrow down the focus to start. To ensure your business collects and analyzes the right data, you need to think about your business use cases. What do you need to learn from your data, and how do you plan to use it? These identified business use cases will help you identify the data you need most.

Consider Potential Data Types

Structured v. Unstructured Data

In addition to collecting and storing your data, consider the format in which you get it and the format in which you need to analyze it. Structured data has very specific parameters and definitions. Unstructured data refers to the format data is in when you get it in its native form. For example, consider a customer complaint via email. The date, time, and sender are all structured data, but the actual message is unstructured. You can easily glean insights from the first three data points, but the email content will take more, possibly manual, work to understand.

You can build an application to use within your data collection process that applies schema to transform unstructured data into a structured and more usable form. Or your teams using the data can do that at a later point in their own preferred systems.

Quantitative v. Qualitative Data

Further, consider the differences between your quantitative and qualitative data. Quantitative is number-based data, like numbers and metrics; qualitative data is often content-based, like the text from a customer complaint.

You may be unable to transform some qualitative data into a structured form. Keep that in mind. It will make it more difficult to analyze, although there are Machine Learning tools out there that make it possible to do things like sentiment analysis on unstructured text.

3. Determine Your Data Storage Strategy

Before you start thinking about where to store the data and how much, you need to know what your business needs to store. This means evaluating which applications are used throughout your company and what kinds of files are created in them. These could be anything from invoices, purchase orders, or contracts to marketing collateral, business reports, and documents relating to employee performance reviews.

Once you have an idea of the types of information being stored by different departments within your organization, determine whether it’s worth retaining for future reference (if so) as well as how long that data should be kept for legal reasons (e.g., financial records). Once you can answer those questions with confidence — or at least ballpark figures — you will be able to plan out where to place these assets. You can choose to keep them on-premises or online, in the cloud.

We recommend storing your data in the cloud to make it accessible from anywhere and ready to immediately leverage cloud-based analysis services. Amazon S3, for example, offers industry-leading scalability, data availability, security, and performance. The product services millions of customers with an ironclad architecture and serves dozens of use cases.

Unused Data

We covered structured v. unstructured data and quantitative v. qualitative data, but we left out a significant category – unused data.

You won’t need all the data you end up collecting, so you must decide what to do with your unused data after any relevant regulatory or other external data retention rules expire. You can choose to store it in case you need it later (which we recommend if storage doesn’t get too expensive) or discard it.

4. Prepare Your Data

Many times there can be data of interest that is generated by production systems. The databases that are engineered to support applications are optimized for the application, not for analysis. It can be detrimental to performance and potentially even to referential integrity if you allow data analysts to run queries against production systems. Furthermore, often the structure of production data is normalized and may not be conducive to analysis.

This is where Data Lakes, Data Warehouses, and ETL processes can be useful. A data lake contains all of an organization’s data in a raw, unstructured form and can store the data indefinitely. This data is generally useful to data scientists for undetermined or unspecified purposes. Amazon S3 is an excellent place to build data lakes because of its durability, availability, scalability, security, compliance, and audit capabilities. With AWS Lake Formation, a business can build secure data lakes in days instead of months.

Conversely, a data warehouse contains structured data that has been cleaned and processed, ready for strategic analysis based on predefined business needs. This data is generally useful to business professionals.

To get from the data lake to the data warehouse, a process is needed to Extract, Transform and Load (ETL) the data. Regardless of the data source, whenever new data lands in the data lake, it can trigger the ETL crawler and transform the data into the data warehouse structure. Robust ETL processes can easily and cost-effectively be built and run on cloud native services such as AWS Lambda and AWS Glue.

The data warehouse can be housed in many different cloud native alternatives, from high-performance in-memory caches such as Amazon Elasticache to something as cost-effective and robust as Amazon S3 with AWS Athena. Athena is the query service that allows users to analyze structured data in S3 using standard SQL.

5. Analyze Your Data

Most data analysis will depend on the type of data you’re working with and your goals for it.

Types of Data Analysis

  1. Predictive Analysis is essentially forecasting. What will happen based on present data, historical data, and patterns running between them?
  2. Statistical Analysis uncovers what happened. It covers a brass-tacks overview of data point to data point, mapping how your business got from there to here.
  3. Diagnostic Analysis determines why something happened. Your team reviews historical data to connect the dots and make correlations to see what influenced the path.
  4. Prescriptive Analysis is much like predictive analysis but uses experimental input of hypothetical factors to determine how to reach different outcomes. For example, predictive analysis basically says, “if everything goes like normal, x will happen.” Prescriptive analytics throws a handful of wrenches into the equation and sees how each situation shakes out with the support of Machine Learning, algorithms, and AI. This helps determine levels of risk with experimentation in business. It also helps with disaster planning.

Analysis Applications and Tools

Many analysis applications are built for specific use cases. For example, Google Analytics mines your website data and how visitors engage with it. You can export this data and import it into other systems (or run a structured integration to make this step automatically) to get even more insights – like on-page behavior via heat map tools.

Or consider how Salesforce mines customer and prospect behavior. You share customer-level data with the system, and it offers insights on how to further nurture the relationship and increase sales.

Likewise, financial data analysis takes place in other systems. You can even leverage operational analysis systems to uncover weaknesses and opportunities in your internal workflows.

Your data analysis priorities should reflect your overall goals. Research the best tools for that kind of data and departmental challenges, and then use a trial of the tool to ensure it’s a good fit for your needs.

You may need a custom tool built. An experienced partner can help you develop an application uniquely focused on uncovering insights critical to your business.

6. Protect Your Data

You also need to prioritize keeping your data protected from cyber threats such as malware attacks or ransomware infections. When dealing with your company’s valuable data, the best way to protect yourself from loss or theft is to avoid storing it in the first place. This means that you’ll need to consider how much of your important data can be kept on-site. You should also train employees about what kinds of information they shouldn’t post on social media sites or even everyone’s favorite workplace chat app: Slack—after all, these types of communication channels are often unencrypted and leave copies on servers that might be vulnerable to hackers.

If you’re unable to reduce the amount of sensitive information your company handles and store it off-site because it’s too large or unwieldy for emailing attachments back and forth between offices (for example), consider using a cloud service like Dropbox instead of sending PDFs via email over public networks like WiFi at coffee shops or cafes.

Also important: make sure all employees know what kind of access they have when working with sensitive data. Sometimes employees accidentally make mistakes with access permissions for shared folders containing confidential files—and many security breaches on companies’ networks are due simply because someone forgot about something as simple as changing their password regularly!

7. Regularly Back Up Your Data

A good backup strategy is the foundation of any data management plan. It doesn’t matter how much you spend on a storage solution if you don’t have a secure way to keep your data backed up. Your backup options include the following:

  • Cloud services: Many cloud providers offer robust backup services that let you store and access files from anywhere, and they can also keep an eye on the integrity of your backups so that you know when it’s time to get more storage or move to another provider. Services like AWS S3 include robust retention rules, versioning, and many other features, such as Intelligent Tiering, which uses AI to decide what storage tier to place a given document in based on real-world access patterns.
  • Offline storage: If you want a physical copy of all your data, there are several types of online drives for safekeeping that won’t suffer from power issues associated with cloud backups like those caused by storms or hackers. If a drive fails, however, it can be difficult and costly — or impossible — to recover everything stored on it.

8. Write and Enforce a Data Management Policy

A data management policy outlines how an organization will handle personal data, including what personal data it collects and why, who has access to the information and how they can use it, how long the company will keep it, where it’s stored, and more.

It’s important for your business to have one because a well-written policy helps you:

  • Demonstrate compliance with regulations like GDPR (the European Union’s General Data Protection Regulation)
  • Protect yourself from liability in case of a breach or other security incident
  • Ensure that all employees are working within the same parameters when handling customer records

9. Educate Employees About Good Data Management

A crucial component of strong data management is educating employees. If you’re not explaining why it matters, your employees will continue to treat data as a throwaway asset. You need to make sure that the people who are using and generating your company’s data know how important good, protected information is for them — and for you.

In addition to high-level company training, consider planning an in-depth conversation with every person on your team about what good data management means for them personally.

One way to do this is by having all members of your team fill out a form that asks them how they use and generate information within the company:

  1. Do they use an application to manage their data? A spreadsheet?
  2. What kind of information do they gather?
  3. Why is this data necessary in their day-to-day lives at work?
  4. How do different types of information influence each other (for example, how does one customer database relate to another)?

These insights will help you tailor data management training for their specific use and address unique security concerns for each employee.

10. Create a Data-driven Culture

Now that you have your data collected and stored and have started the ongoing and consistent analysis process, it’s time to focus on creating and strengthening a data-driven culture within your company. After all, if you’re not going to leverage your data to influence decisions, why collect it at all?

This culture starts at the top. Executive leadership must focus on basing strategy on data and sharing these insights and data journeys with the company. This will flow down to departmental leadership and reinforce a focus on data collection and analysis when and where decisions are being made.

When navigating a challenge or opportunity, prioritize the question and discussion around “What does the data tell us?” and “Are we missing something that the data might reveal?” More people within the company will need access to your data to ensure cultural adoption, so don’t forget about permission-based roles in your access methodology.

Praise and reward people who make interesting findings with the data and give them the resources they need to experiment with these findings. Show the journey of a successful company endeavor, from a data insight to the initiative’s key success metric to drive the point home.

This doesn’t mean you have to discount “gut” feelings – after all, businesses need to operate with some level of risk, and risk insinuates little evidence or proof to work with. However, we encourage you to emphasize all the opportunity data provides.

Moving Forward with Your Data

Data management is an essential part of running your business. Without it, you won’t be able to make smart decisions about your future and grow successfully. By following these best practices, you can ensure that your company stays on track with its goals, uncovers the insights it needs, and stays protected from cyberattacks as well as other external threats. If you need help getting more out of your data – through better analytics applications, storage solutions, or strategic cloud services – our team might be a good fit. Contact us to speak with a consultant.

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