The hidden correlation between machine learning and data management

December 11, 2023
This is a repost from our friends at TimeXtender.

For decades machine learning (ML) was slow to evolve because of the complexity of emulating human thought and the difficulty in processing sufficiently large data sets. Today, though, thanks to major advances in software development, computing resources, and the availability of huge, rich data sets, an increasing number of organizations are investing heavily in machine learning.

ML has grown to such an extent that most consumers have come to rely on it for many services they use daily from search engines to recommended systems and social media platforms to name but a few. Likewise, organizations are using it in their client relationship management, communications, management, marketing efforts, and overall, in making better business decisions.

The thing is, successfully implementing machine learning solutions depends on data, and lots of it. One could basically say that data is the lifeblood of any machine learning model. And with huge amounts of data, comes data management. At its core, data management is about preparing data properly. If this fails, machine learning results will surely suffer.

So, why is it vital to prepare data properly and how is it done? This article looks at these questions in more detail and offers a short guide for organizations to get their data management on track.

Why prepare data?

When faced with a problem that needs solving, raw data is collected based on what the problem is and what prediction the machine learning algorithm will need to make. For example, making predictions on home prices would require a substantial amount of home sales data – including prices and a thorough set of attributes about each home sold.

Once collected, this data can’t be used as is, and the raw data will have to be changed before the organization can use it as a basis to make predictions. The three main reasons why this needs to happen is:

  • Algorithms expect numbers. Although a specific data set can contain data of many different data types, machine learning algorithms typically expect numeric data. In other words, they take numbers as an input, and predict a number as an output.
  • Algorithms have requirements. There’s a variety of algorithms available for any given project or problem. When a project is planned, multiple algorithms need to be evaluated to determine which will give the best results, given the data set and the question that needs to be answered. The problem is that each algorithm has specific data requirements, so when chosen, the data must be prepared for that algorithm.
  • Model performance depends on data. The performance of a machine learning algorithm is only as good as the data that’s used to train it. It could almost be likened to a car. If it’s filled up with dirty fuel, it’s performance will suffer. In machine learning models it means that predictions will suffer if the input data isn’t prepared properly or if there is not enough data.

Given that most machine learning models are well established, well understood, and widely used, the key differentiation is the data that’s used to train it. This, ultimately, means that data preparation is crucial and can mean the difference between a successful implementation of a machine learning model or a total failure.

Data management best practices

Keeping this in mind, there are some best practices when it comes to data management to make sure that the data is properly prepared, and that the best possible data is available for analysis or to be used for any specific algorithm.

Identify machine learning use cases

Because many analytics problems are suited for self-learning algorithms, organizations often think that it’s as simple as choosing an algorithm and feeding it the data. In doing this they neglect the question whether the application of a specific algorithm is feasible. In practice, the data available will often dictate which algorithms are best suited for the application or use case. In other words, studying the data will often reveal which algorithm should be used.

Define the data set

Each algorithm requires a data set, its sources, and the frequency with which it will be updated. It’s important to keep in mind that these requirements will vary depending on the algorithm used. For example, where real time analytics are required, the algorithm may require live transactional or clickstream data. Likewise, predictive applications may need historical data to make predictions. In simple terms, it’s vital to assess what is feasible, considering the timelines and budget of the project. This will, ultimately, impact what data is used, how the data set is defined, and how it’s prepared.

Define data preparation requirements

Apart from determining what data will be used, the specific use case will also, to a large extent, dictate the preparation steps including the data collection, refinement, and delivery for production analytics. Once the right procedures have been established, procedures to deal with missing values, data profiling and quality measures will need to be established in order to assess false positives and data skew. These requirements, ultimately, sets the tone for the steps to come in analyzing the data.

Define logical use patterns

Once the use case is identified and the data set and data preparation requirements determined, it’s necessary to define how the data will be used. In other words, it’s necessary to decide on the input data types, the decisions the algorithm will make, and the temporality of those decisions.

Develop a data pipeline for each use case

When all the above steps are complete, it’s vital to define the data pipeline requirements for each use case, both for model training and production. Also, keep in mind that, because different use cases require distinct data sets, processing capabilities, and decision frequencies, they might require different data pipelines. For this reason, all pipelines should be studied to give organizations the opportunity to identify possible overlaps and to combine pipeline components to make the entire process more efficient.

Select data platforms

Next up is to choose data platforms based on the data sources that will be used and the data processing requirements. Here, there are many options from open source to commercial solutions. For example, data lakes can efficiently store large quantities of raw data for exploration. That data can be processed, integrated and transformed into complex data sets ready for focused analytics in structured data warehouses. Also, organizations can choose to implement a modern data estate which speeds up development and ensures the data is always ready for analysis.

Plan for fast growth

When it comes to hardware planning, make sure to plan for sufficient capacity in the processing power, storage, and network bandwidth for each data pipeline. Machine learning projects rely on data and uses more to learn and improve while in production. So, it’s vital in these projects to make provision for high growth in data volumes and sources. Cloud data platforms provide scalable processing and storage without requiring the purchase of hardware.

Streamline data flows

Change data capture checks for and copies data and metadata changes in real-time from relational database management systems and other production sources and eliminates the need for batch replication. This increases scalability and improves bandwidth efficiency by only sending any changes from the data source compared to using the entire data set.

Carefully consider requirements for model testing

A cornerstone of successful machine learning models is testing to ensure the best results, both before and after going into production. This requires continuous adjustments to the underlying data sets and a complete change history.

Plan for fast iterations

Just like machine learning models teach themselves based on the data provided, practitioners should also work through their own trial and error process. Here, they’ll make continuous adjustments to the data input sources, algorithms, and try new algorithms. By constantly comparing algorithms with one another, practitioners will have the data to better decisions about how to improve the results.

Monitor and refine data flows

Organizations should consider solutions that centrally configure, monitor, and analyze tasks which makes it easier to better manage performance, troubleshooting, and capacity planning. With this ability, these organizations can ensure data remains available, current, and ready for machine learning analytics.


By following these best practices, organization can optimize their data architecture to deliver the best results to their business. Ultimately, these practices make it easier to deal with new use cases and accommodate new data sets. And this is where TimeXtender comes in. It’s an automated data management platform that helps organizations to implement and operate data lakes, data warehouses, and a semantic access layer. In the process, it helps automating the entire process of getting data ready for analysis, without writing a single line of code.

Built specifically for Microsoft Data Platforms, TimeXtender’s automation platform speeds up development of a modern data estate by more than ten times and generates complete documentation for the estate. Plus, TimeXtender helps future-proof your analytics data on Microsoft platforms by allowing you to change data platform without rewriting data pipelines.

Hopefully this short guide provided some insight into the best practices to be implemented in a data management solution. Please visit TimeXtender’s “resources” section on their website for more information on data management or to find out more about our automated data management platform.

Looking to learn more about data engineering? Check out ourGuide to Data Engineeringwith helpful resources on this topic.


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