For team leaders and decision makers, a sharp instinct and observation skills are great assets. However, adding data to the equation makes all the difference. Most companies recognize that data has an enormous importance for their business, but a large percentage admits having problems managing data.
There are dozens of concepts and variables around data management, which can get confusing. That's why, before exploring some of those key definitions, let’s bust some myths around data management:
- “A data management plan or a strategy is only for big companies.” — Wrong! The sooner you become aware of the importance of having organized, manageable, and integrated data, the better. Startups and small teams can increase their productivity and ROI by managing their data properly.
- “Managing data is having a CSV file in order.” — Definitely not. Though everyone loves a tidy CSV file, the manual work involved often results in mistakes, outdated data, and security risks.
- “You need a big IT team to tackle data management.” — Not necessarily. There are a lot of easy-to-use SaaS, iPaaS, and cloud-based services that allow you to collect, store, maintain, and integrate your data.
Now that you know that everyone can benefit from data management, let's talk about what it is, why it's so important (and sometimes so difficult) to get right, best practices, and top vendors for different business sizes.
What is Data Management?
Data management is planning and controlling the way data is collected, stored, protected, and processed. In business, data is usually associated with customers, prospects, employees, providers, deals, accounts, competitors, and finances. When an organization effectively manages data, they obtain analytical information to make the right business decisions.
Data management encompasses three basic stages: collection, storing, and processing. However, protecting your data should be a priority throughout the entire process.
Since business applications and the databases within them come in all shapes and sizes, each company should take its own approach to these stages. You should do so considering your particular technology ecosystem and, if necessary, defining and adding new steps to the process.
Data cleansing, for instance, might come between the storing and the processing stages for some companies, while others consider it a part of the processing stage.
There are techniques and best practices that can help you manage data, but there's no such a thing as a data management plan that suits every business. There are, however, common challenges that almost every business faces when it comes to managing data.
How to Prevent Data Management Challenges
According to Forbes, "the gold is now data," and they predict that by 2025, the global market for data and data analytics will be worth $135 billion. Top business managers are willing to invest in data because of its indisputable value. But there's a catch: data can be very difficult to manage properly, especially if you didn't start thinking about data management at an early stage of your company. Then, you might end up with an enormous amount of data in a completely unmanageable format.
To prevent that, be aware of the challenges that come with data management.
Each of the data management stages mentioned (collecting, storing, processing, and protecting data) has its own particular challenges that vary depending on the approach you take. Alongside these, there are more general challenges associated with data management that most companies, big or small, will have to face:
Ensure Data Integrity
Data integrity is determined by how consistent data is. Having data integrity requires a smart data collection process. When filling out a web form, have you ever misspelled your phone number and gotten a message saying something like “The phone number you added is wrong, please try again”? That's a clear example of one of those “smart collection processes.” This type of filter is created to make sure the data you are gathering is valid.
Only if you've ensured the integrity of your data it's considered relevant enough to work with.
Achieve Data Quality
When talking about high-quality data, there are three concepts to highlight: accessibility, consistency, and relevance. Thomas Redman, in his book Data Driven: Profiting from Your Most Important Business Asset, frames the concept of data quality well by saying that data can be considered high-quality if it is "fit for its intended uses in operations, decision making and planning."
After ensuring data integrity, you'll have consistent data. But if that data is not accessible, it doesn't suit its intended purpose. Similarly, if data is consistent and accessible but not relevant for your operations, decision making, or planning, it also loses its quality.
Integrate Disparate Databases
The average company works with multiple applications. That's why software integration is one of the biggest data challenges your company might face. Each one of your applications has a database with particular characteristics and rarely connects natively with all your other apps. However, to have a complete overview of your data, you need to unify your software stack.
Integrations can be as complex as they are necessary. But fortunately, with the rise of Integration Platforms as a Service (iPaaS), they are now a lot more accessible for small and medium sized businesses.
Comply with Constantly Changing Compliance Requirements
Managing data has to be taken seriously, especially if you are working with your customers', prospects', or leads' personal data. Not following the compliance requirements established locally and internationally can have legal consequences.
Businesses have the responsibility to keep up with compliance requirements that are constantly changing to ensure the proper management of data. Over the last few years, at least three major data regulations have been implemented: the General Data Protection Regulation (GDPR), the Data Protection Act 2018, and the California Consumer Privacy Act.
Data Management Best Practices and Techniques
There's a lot to keep in mind when you want to implement or improve your company's data management. The good news is that lots of companies like yours are successfully managing their data and you can learn what has worked for them.
Your company's data management plan or strategy will always depend on your unique software stack, database, and company size. However, the following best practices and techniques are good starting points to adapt and customize to your business with the level of complexity you need.
Vendors like Google offer data catalogs as a complementary product for data management. These products are essentially search bars to make data assets easy to find and categorize.
If you are running a small business, you can replicate the function of data catalogs by creating an inventory of all the data assets your company has. This data catalog can help your different teams easily find the data they need to access. Tags and labels are a great way to categorize groups of data to find them easily later on.
Having a clear and complete inventory of your data assets is also very useful when you want to build workflows or integrations between databases.
Data is rarely collected by a single platform. Usually, there are several applications in place for specialized processes or operations that each have their own databases and gather a fragment of your company's data.
Let's say you have an online shop where you sell running shoes. You might have one app gathering the information your customers fill out when they make a purchase, a second app for billing or accounting, and perhaps a third one with a chatbot to answer questions. All these apps collect a piece of data about one single customer. The goal of integration is to keep all those fragments together and offer a complete view of your customer. When data is integrated, its quality improves.
If your company is working with in-house software applications, integration might require a team of engineers with an ad-hoc solution. For those small and medium-sized enterprises who choose to work with cloud-based platforms, iPaaS can be a great solution.
Data Lifecycle Management
Data lifecycle management (DLM) is mostly used by big companies working with massive amounts of data that needs to be categorized into tiers, often with complex automations. But for smaller businesses, it can also be a useful structure to keep in mind while you are developing a strategy for data management.
In simple terms, DLM identifies the different stages that information flows through and creates policies to manage each one of those stages. The ultimate goal of this framework is to maximize the useful life of your data.
The stages or steps of DLM are:
Customer Data Platforms and Data Warehouses
Data warehouse and a customer data platform are two of the most common ways data is collected and stored.
A data warehouse is essentially a database a company transfers all of its data - usually from disparate sources - into. For example, at LawnStarter we put our web activity, financial data, product analytics data, CRM records, help desk records, and even search engine rankings all into a single Amazon Redshift database so that everything can be queried together. Data warehouses are often called data lakes or data marts.
A customer data platform is a more user-friendly platform that also collects data relevant to your customers and displays the data to end users in more relevant ways Often a customer data platform is simply the abstraction or ‘front end' of a behind-the-scenes data warehouse.
In both cases, typically a business is taking all the data from its CRM, help desk, web analytics, financial and other internal systems and putting them together.
Data Management Software for Enterprise
For enterprise, there are thousands of software companies focusing on the different operations and stages of data. Often, the same software supplier offers several products to manage one or more operations. Such is the case of Oracle, IBM, SAP, or Microsoft with its Azure services.
Among the most popular all-encompassing solutions, you'll find master data management software (DMD), storage and integration services. Since there are so many options and providers for enterprises, you'll find that some DMD already offer storage and integration solutions while some integration vendors cover some of the processes of a DMD.
Master Data Management Software
Your master data are usually those key assets that help you elaborate business information. A Master Data Management software typically includes solutions for data consolidation, cleansing, accessibility, verification, and organization. Here are some of the most popular names around:
SAP Master Data Management Software: These tech experts basically cover the entire production chain offering twelve different products. Its MDM helps you consolidate, enrich, process, and analyse your master data. This product is often complemented by their SAP Master Data Governance.
Ataccama ONE: They describe themselves as a "self-driving Data Management and Governance" tool. They are a growing company specialized in several data stages and operations. Their MDM is called Ataccama One. It offers a data stewardship service with an accessible data catalog and even some artificial intelligence (AI) features.
Oracle MDM: This company has been around since 1977. They are truly experts in database software, technology, and enterprise software. They offer an MDM for products from the Oracle Data Hub Cloud. Even if you are not using their products, you might want to check out Oracle's unique resources before adopting another system.
There are so many solutions out there for enterprises that you'll need to have a solid data plan to determine which specific features you need. Only then, you can compare different providers. A good starting point is visiting review sites like GetApp.
Storage Management Software
These services help you manage your storage capacity in a cost-effective way. The four types of storage management are cold storage, block storage, cloud file storage, and hybrid cloud storage. G2Crowd lists the following as the best ranked for each category:
Amazon Elastic Block Store (for block storage software): In this context, a 'block' is a unit of data with a specific volume. It's basically like buying different USB flash drives (extremely big USBs) where you have room to store a certain volume of data. Amazon's EBS allows you to choose between four different volume types. Their software is easy to use, scalable, and cost effective.
DriveHQ (for cloud file storage): This type of storage works with a shared file system. That enables multiple users to access all or a selection of files with different volumes of data. DriveHQ can be used with cloud and local files and it offers backup options for both types of data at a very accessible price.
Amazon S3 Glacier (for cold storage software): Cold storage is ideal for your inactive data. It allows you to store safely the data that you are not actively using with a low budget. Glacier's main benefits are durability, scalability, and security. Plus, you can always retrieve it in 1 to 5 minutes.
Before you jump into a third-party integration service, make sure you have a clear integration plan, that you understand the challenges of data integration, and that your goals are well-defined. Some names you'll come across when you start your research are:
Operations Hub: With its diverse features, Operations Hub serves from small businesses to enterprise-level operations teams. Next to the basic two-way sync, its starter plan offers custom fields mapping and advanced filtering for different types of data.
Boomi: This product by Dell is one of the leading iPaaS for enterprise. Boomi keeps together all your teams, channels, devices, and applications with a complete and scalable platform. They take care of data transformation, integration, migration, security, and visualization.
Mulesoft: They mix iPaaS and Enterprise Service Bus (ESB) functionalities to offer a broad network of applications and data on-premises and cloud-based. They have dozens of features, but they are mostly known for their applications integration and data transformation.
Jitterbit: They are one of the fastest to implement. Jitterbit can be extensively customized, which makes it very flexible. The service combines features of integration and AI to connect your SaaS and other applications in-premises or cloud-based.
Data Management Software for Small Business
Small businesses are increasingly using SaaS. These services work with open APIs, have native integrations, and are compatible with iPaaS. That means that it won't be hard for you to manage all the aspects of your business using different SaaS applications and keeping them together through different integration services.
Storage Management Software
SaaS usually have their own database to store customer, employee, or business data. For the rest of your SMBs data, you can use the version for business of one of these tools:
Google Drive: Anyone with a GSuite account has access to the basic Drive version with 30 GB storage capability. On the Business and Enterprise versions of GSuite, you can get up to 25 TB of cloud storage. Since it's so easy to use, accessible, and compatible with all the other GSuite applications, Drive is a great storage management tool for small businesses.
Dropbox: They have a free version with 2 GB of storage you can access from three different devices. For business, you get almost limitless space through a very secure system. Since it works with block synchronization, the speed is remarkable.
Microsoft OneDrive: for Microsoft users, OneDrive is a reliable and affordable solution. It offers up to 6 TB and it works smoothly with the MS Office productivity suite. Plus, Microsoft is known for taking data security very seriously, and OneDrive is no exception to their security protocols.
It's never too early to start thinking about data integration. The sooner you define and implement a software integration plan, the better. If the solution you need is not available natively or in-app, these are some iPaaS you might want to consider:
Operations Hub: This native integration empowered by HubSpot saves you a lot of trouble. Its free plan offers a complete synchronization solution that works two-way and in real time. Which means that if you update your data in either app, you'll have that change available in the other one. No matter who entered the data or from which device. It also works with historical data to avoid duplicates or gaps of information. For now, it syncs contacts, leads, and accounts.
Zapier (for one-way push): It's great for one-way, one-time actions. You can set up a zap to make sure that whenever you add a new piece of data into app X, there's a copy of that piece of data in app Y. It works with several objects such as contacts, Trello cards, Slack message, Google Sheets data, etc.
Automate.io (for one-to-one automation): This integration option can help you create complex workflows between apps. For instance, if you want to get a notification on Slack every time someone subscribes to your newsletter, Automate.io is the tool for you. It's ideal for one-way and one-time workflows between the 100 apps they support.
Benefits of Investing in Data Management
Now that we've covered a number of common concepts and definitions of data management, here are some of the benefits of investing in proper data management practices.
Understanding Your Most Profitable Customers
In the book The Inside Advantage, author Robert H Bloom asserts that one key to business success is understanding the customers who are most profitable and whom you enjoy working with most.
For a modern technology business, that answer may be easier said than done.
You can't simply look at which customers spend the most money with you. You also need to assess the cost of supporting those customers, which likely comes from your help desk software and potentially your payroll system. Additionally, larger customers likely cost more to acquire - that data point comes from your CRM, marketing automation, and advertising platforms.
Only when you put all of these together can you fully understand and identify your most profitable customers.
Evaluating Customer Acquisition Channels
The lifetime value of each customer depends on which channel you acquire that customer from. Especially when it comes to interruption marketing and paid acquisition in general, or when offering first-time signup discounts. A CDP or data warehouse allows you to connect your customer acquisition costs with your customer retention data, and understand your full ROI.
Grasping Your Full Buying Cycle
In most cases, customers typically don't simply click on an ad and immediately purchase your product, especially in B2B. There is a buying cycle that can sometimes last months.
In an enterprise funnel, your company is typically reaching your customers via cold email, ads, phone calls, nurture emails, trade shows, in-person meetings and proof of concept demos. It's an extremely complex process which can only be understood by managing one's data properly.
Other Data Management Concepts
Data Migration: The process of moving data from one database to another.
Extract, Transform, Load: A process that involves pulling data from a database (extraction), manipulating it via code in some way (transformation), and writing it back to a database (loading).
Metadata: Data that describes other data within a database or data warehouse.
Fact Tables: Tables containing core business metrics that have been prepared in a way that they are easily understandable and user-friendly so that stakeholders across the business can access. These are often called "single sources of truth."
Business Intelligence: The practice of analyzing and presenting data in a way that provides insight into making business decisions. Often the product of a business intelligence team is a metrics dashboard or a report with insights.
Schema: The structure that defines how a database is organized.
Data Cleansing: The process of preparing data in a way that incorrect or not useful data points are removed.
Data Governance: The rules and procedures that define how a company's data is managed. Often a team or individual will be responsible for data governance, and that person will be responsible for things such as access requests, definitions of column names, and maintenance of database records.
Data testing: The practice of making assertions about your data, and then testing whether these assertions are valid. This concept can be used to test both the quality of your source data and to validate that the code in your data models is working as intended.
Data Management is for Everyone
Data is at the core of your business. Whether you are an entrepreneur or if you are part of a big company, a basic notion of data management can help you increase your productivity, improve your customer experience, and make life easier for your employees.
There are lots of concepts to learn and many challenges to manage data effectively. On the other hand, there are also best practices to guide you and a variety of software solutions to help you along the way.
Across all the stages of data management, two aspects to highlight are security and integration. Security must be inseparable of a responsible use of data, while integration must increase data quality and integrity through an almost imperceptible setup.
There's not a single data management plan that fits all businesses, but there are dozens of options for each business. That's why, you need to understand your company's needs, its applications and the data within them to find the right solutions for you.
Originally published Oct 26, 2020 7:31:00 AM, updated April 21 2021