In today's business world, data is everything. It helps us make informed decisions, track our progress, and measure our success. But all too often, businesses need help with data management. From duplicate records to inaccurate information, the problem of data mismanagement can seem impossible to handle. But it doesn't have to be. By taking a few simple steps, you can get your data under control and start reaping the benefits of accurate, up-to-date information.
Define Your Data Needs
The first step in improving data management is closely examining your business's data needs. What kind of information do you need to track? What are your goals for data collection? Answering these questions will help you develop a plan for collecting and storing information.
Businesses want to collect every data they can. But this approach has proven wrong. Don't try to capture everything! Be selective with a little buffer.
The cost is the most direct hit when dealing with excessive data. Storage costs are cheaper with cloud-based database services. But we mustn't take it for granted.
You can choose the optimal cloud storage option when you know your data needs. For instance, you'd prefer a data warehouse with structured data sources and specific needs such as business intelligence. But when you're primary motive is to store images for statutory purposes, you may choose a data lake. It's cheaper and can accommodate a wide variety of unstructured and semi-structured data.
Related: The Difference Between Data Warehouses, Data Lakes, and Data Lakehouses.
The most common data needs involve the following.
Reporting, Analytics, and Business Intelligence.
Accumulating data for business decision-making is one of the oldest ideas of why we need databases. Today we have sophisticated techniques to collect, store and use data to support business decisions at every level.
The leadership team may want to see past performance in interactive dashboards. Modern-day dashboards are connected to data warehouses such as AWS redshift or Azure Synapses. They store processed data for faster access.
Related: How I Create Dazzling Dashboards Purely in Python.
Likewise, the front-line staff needs access to a wide array of user and organization data to serve clients faster. Beyond data visualization, their dashboards usually contain controls to take action immediately. Thus, we usually connect it to a transactional database instead of a data warehouse.
Training machine learning models
While the BI tools take care of the retrospective view of your businesses, leaders are also interested in the future.
Once again, the strategic level team might only be interested in the future directions of the business and its operations. But artificial intelligence plays a role in many functions of modern enterprises.
When you need to serve your customers better with recommendations, you may train a model with the data you have in your warehouse.
Primarily, usage data are structured. That is, you can organize them in tables and relations. But sometimes, you may also want to use text and images to train your model.
For instance, if you want to find product defects from customer-uploaded images, you should store examples in a data lake.
Data is your product.
Sometimes, your data is your product. You may have a streaming service, for example.
Strategies for storing and serving data differ significantly for different needs. If you are storing videos and streaming them, like Netflix, you should use a database like Cassandra. But if you let people download your resources, your data lake should be enough.
Data is a first-class citizen in most applications we build today. It means every other component depends on the data we have. Thus we must spend more time on what data needs we have. The needs we identify impact the type of data storage to use, pipeline strategies, staffing, and ultimately the business's profit.
Implement a Data Management System
Once you know what kind of data you need to collect, it's time to implement a management system. There are several different software options available to help with this task. Choose one that fits your budget and meets your specific needs.
Data Management is much more than storing data. It's an end-to-end process of ensuring high-quality data enters the pipeline at the right time and is discarded when needed.
Related: How to Improve Data Quality Without Firefighting Them?
Depending on the volume, velocity, and variety of your data sources, you will have to choose from different systems.
There are tools to handle every part of the data lifecycle.
Garbage in — garbage out!
The most crucial point of a data-driven company is when the data is acquired.
A particularly vulnerable area is where sensor data is used. If the sensor configurations are incorrect, you'll receive erroneous data. Since sensors would send data at a very high frequency, your data warehouse would soon be filled with garbages.
Yet, such things can be avoided by establishing standard procedures. For instance, you can let the data go through a monitoring period whenever there's an update to the configurations. Data coming through this extra pipeline can be merged with the master data after we ensure its quality.
I've found using established tools to connect data sources more robust than building our connectors. Like me, those from a programming background tend to code it without going for a third-party solution. But what ultimately wins is that we should do what we do best. And for most companies, building their connectors is not a strength.
Fivetran helps us connect and load data from several sources. Connecting data sources was a cumbersome task before Fivetran. But now, we can pull data from hundreds of sources with only the configuration details, such as auth tokens. You never have to worry about the implementation of the connectors.
Once you connect your data sources, the next step is to transform them. You can use Dbt to do it much more quickly. In Dbt, you can create and reuse SQL snippets. You can even generate parameterized SQL queries that would take weeks.
We've already discussed a lot about storage options. But to summarize, you might want a data warehouse if you need faster data retrieval and a data lake for unstructured sources.
You can store structured and unstructured data inside the same resource without compromising performance in data lakehouses. They help avoid data duplication and redundancies.
You can use a relational database as a backend for your applications. Postgres is a popular open-source relational database. You can also choose MySQL or MsSQL for this purpose. You wouldn't be using these resources for transactional data.
If your schema demands more semi-structured databases, you can choose NoSQL databases like MongoDB, Cassandra, etc.
It is best to choose cloud databases over on-premise ones. Cloud databases are flexible, robust, and cheaper options to consider.
Master Data management
Congratulations so far on building pipelines and acquiring data for your data warehouse. But you must ensure its quality before others consume it.
Today we have sophisticated tools to ensure data quality. This process is popularly known as master data management (MDM). You can connect these MDM tools to your data warehouse and manage data integrity over these tools. You also get additional features such as who can edit which part of your data warehouse and approval cycles, etc.
Tools such as Ataccama One and Profisee integrate well with other pipeline components. They even have automated flags showing up on potentially flawed records.
Having a data quality dashboard is advisable to show you data drifts. Even simple measures such as the moving average could give you great insight if your data is consistent with prior data.
Data drift isn't a concept that can be handled within an MDM tool. But you could notice anomalies and start investigating them before poor data gets into your core business operations.
Once your dataset is in a data warehouse or a data lakehouse, and you've got measures to ensure its quality, you must consider its consumption. Data consumption is a critical aspect of enterprise data management.
Whom do you allow to consume, which part of the data, and for what purpose are all daunting questions to answer. Getting it wrong can create serious consequences. A data breach could bring down a company, and it could even create legal issues alongside.
Allowing access to raw data is not usually a good practice. Instead, allowing data consumption through APIs or dashboards would be best.
When developing a data-intensive application, the dev team can work on a dummy database and connect the application for user acceptance tests. This way, even developers don't get access to more data than they need.
What matters in most cases is the schema, not the data.
A particular case where bulk data access is required is for machine learning training. Machine learning training needs batch data.
Often, to avoid model overfitting, you need more data. Overfitting happens when the model memorizes the examples rather than generalizing them.
Once again, it's best to create database views and grant the data science team restricted access only to this view.
Continuously Train Stakeholders
Data management isn't something that can be left to IT alone. Every employee who deals with customer data needs to be trained on proper data entry and storage procedures. Implementing regular training sessions will help ensure that everyone is on the same page when it comes to managing your company's information.
Data quality is an enterprise-wide commitment, not of a single person or a team.
Poor quality data can enter the systems not only through wrong data entry. Incorrect transformation logic and system failures can contribute to data quality issues.
Hence the wider team needs to know the consequences of their actions.
Levels of training
Data management is everyone's responsibility. Thus, all employees should get training regardless of their rank. But their training material does change with their roles.
Front-line staff often deal with more granular data. They both insert and consume data frequently. Hence, they should have training about how to spot poor-quality data as early as possible.
Data engineers play a crucial role in data management. Okay, that's too obvious.
Although ensuring data quality is part of their job role, they, too, need training on the impact of poor-quality data. And knowing them helps create meaning in data engineers' work. It's unique to the organization.
Mid-level managers deal with other people who deal with data frequently. They are responsible for encouraging people to bring up data quality issues without hiding them. They should know what to do when quality issues arise and when to escalate.
The senior management is not exempt from data quality training. The leadership team's training should involve what data we collect and its proper use. Ultimately the senior management's directions for the company could seriously alter the data needs, data quality, and data consumption strategies.
How to identify current and potential data quality vulnerabilities?
It would be very challenging for the leadership team to think of all the possible things that could go wrong. Often the leadership team is disconnected from the front-line staff. But this shouldn't stop them from having a complete view of how their staff handles data.
Hence, it's a good idea to have data quality circles. The primary motive of this circle is to continuously learn and document different data issues that arise within the organization.
Data quality circles would involve people from multiple practices. They discuss issues and collaboratively find solutions. Solutions can later be discussed with the broader team and implemented.
Data quality training frequency
Data quality training should be repeated. We shouldn't think of them as a one-time certification course.
The dynamics in the data science landscape are rapid. Each day, we get new data sources and new use cases. With them comes vulnerabilities.
Further, as human beings, our commitment to certain things diminishes over time. If not updated with fresh material, you can't expect people to maintain the same standards forever.
Most practices would only need an annual training program. Yet, if an approach creates mission-critical data for the organization, it must be trained more frequently.
For instance, technicians who configure the sensors may need more frequent training because their work involves high-volume data creation.
Perform Regular Audits
Even with a solid data management system, things can still go wrong. That's why regular audits of your data collection and storage procedures are crucial. These audits will help you identify any weak spots in your system and make necessary changes to keep your data accurate and up-to-date.
Audits are beneficial for the proper management of enterprise data. Besides having proper protocols, and continuous learning, issues can still sneak in with no symptoms.
During an audit, you can uncover issues that go unnoticed. You can do both data audits and process audits.
Data audit involves taking summary statistics and comparing them with benchmarks. Prior month statistics and comparable industry statistics are significant benchmarks for data quality.
Process audits deal with the methodologies used for data collection, their transformations, security, and other pipeline activities.
Data audits are a must to ensure data accuracy and integrity. Besides, we must also follow regulations like the CCPA and GDPR. Failing to do so would be catastrophic for a business.
An annual data audit can ensure that you follow the regulations and the risk of a data breach is minimal.
Invest in Data Recovery Services
No matter how well you manage your data, there's always a chance that something could happen to cause it to be lost or corrupted. Investing in data recovery services from a reputable provider is essential. These services can help you recover lost or damaged data to keep your business running smoothly, even in adversity.
Most cloud services are covered with an SLA. But it would be best if you did not take them for granted. You must ensure proper backups for a business's continuous operation. You can choose to store multiple backups at different locations. Well, with cloud computing, this is now feasible and affordable.
Most cloud service providers offer automatic periodic data backups. This approach could help save time, and you may not need to hire another database administrator to do this.
If you store data locally, you must ensure the physical safety of the data center. How much effort you put into the data center's safety largely depends on the nature of your business and the importance of the data. For instance, you might want the best possible security for a bank's data center. But with the logs database of an entertainment company may only need basic security.
Think about the worst case and be prepared.
Data management is critical for businesses of all sizes. You can improve your company's data management practices and reap the benefits of accurate, up-to-date information by taking a few simple steps.
All companies we see now are data-driven. At least, that's what we believe.
But managing enterprise data is an arduous task. More extensive data reserves incur more costs, attract more attackers, and are error-prone. But with the five steps outlined in this article, you're less likely to go wrong.
This post doesn't aim for any specific industry group or people. They are solutions for frequent issues in several organizations.