Conducting Data Management in Dev Teams
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With data being modern-day companies' number one asset, it comes as no shock that businesses from all over the world are starting to highly value their data above any other tangible asset that they may own.
So, how can a development team (engineers and software developers that work closely on a given project) effectively work together to achieve their final goal, especially when it comes to handling how the data is used and processed? When it comes to dealing with stored data certain practices and methods should be applied for near optimum results. The practice of maintaining data is known as data management.
Data management is the process of collecting and maintaining data in an efficient and effective way. Nowadays, companies spend small fortunes to store their precious data, using combinations of databases and third-party database providers and highly experienced and specialized developers to maintain such data for further use.
In this article, we will explain why conducting proper data management is of absolute importance in machine learning, deep learning, and data science, in general. Further, we'll understand the steps that must be followed to ensure proper data management.
Why Should Companies Apply Data Management?
To explain why companies require data management, let's start by explaining how companies can use their stored data.
While there are many benefits of companies storing their own data, the main ones being getting a big chunk of data regarding multiple different users to obtain a general customer pattern on a given product or service. Companies can benefit from processing every customer's personal data to obtain individual customer preferences (used in recommendation systems like those on Amazon and Netflix). Finally, some applications or websites that a company operates do require individual user data to function (Twitter, Facebook, etc.).
By applying effective data management companies can easily maintain accurate, up-to-date, and trustworthy data. By utilizing accurate data, a business is sure to get near optimum results. Moreover, having your data up to date allows you to catch time-dependent patterns such as customer trends and so on. Finally, by checking the trustworthiness of the data collected, you're making sure that your end results will provide accurate feedback for further decision making. Do not forget that a lot is hanging on such results, especially when it comes to the business world.
Data Lifecycle Management
While the specifics may differ from company to company, in the following part we will discuss how you can apply proper data lifecycle management by explaining the four main steps used in any management system. Note that this process is a continuous process that keeps on running.
Step one is the process of collecting raw data (data ingestion). Depending on the required end goal, different data points are collected. This seems easier than it actually is. Companies spend a lot of time and effort collecting user data. In some cases, obtaining a pre-built data set could save you a lot of time and hassle. Do not forget to follow the 3 data requirements mentioned above while collecting data. Different data formats and types are also available such as text, audio, rating, etc.
For our second step, we have data filtering and cleaning. In this process, all unneeded data or data that may cause our model's accuracy to falter will be filtered out. As storing and processing data is a highly expensive process, any extra and unnecessary data should be eliminated, this may include entire columns that have no further use or complete rows with strange values. Further filtering of such data may include extreme points that are known to be untrue or data points with zero or infinite values (in cases where the data is known to be wrong). It is worth noting that the more filtered and clean our data set is, the higher quality results our model will yield.
The third step is storing the filtered data. Depending on the size, structure, and confidentiality of the data, different storage procedures are applied. The two main different storage types are relational databases and non-relational databases. Relational databases also known as SQL databases work best with highly-structured data and data in need of high security. While non-relational databases, also known as NoSQL databases, can easily store a massive amount of unrelated unstructured data. In some cases, a combination of both database types is applied.
The fourth and final step is the processing and analyzing of the data. This step is the end point of our process and it's why we go through all the effort of performing the previous steps. By analyzing the filtered data, you can discern certain patterns. As stated before, such patterns may give insights into the general use of products, buying habits of the general public, and specific likes and dislikes of individual users. Nonetheless, here is where all the creativity arises. With these ways of searching and extracting hidden insights, new business paths and decisions can be made.
Other steps that may be included in the data management process include quality control, governance, transformation, etc.
Tips for Conducting Data Management
Data Security
While data should be easily accessible to all company employees (with proper access), companies need to make sure that their stored data is safe from outsiders.
Data Quality
Although mentioned previously, continuous checks on the source, trustworthiness, and errors that may be found in the data is necessary to maintain high-quality results.
Start with the End Goal in Mind
By knowing what you are looking for, the process of collecting the necessary data will be much easier. In some cases, companies store every data source they can get their hands on, leading to higher costs in the long run.
Conclusion
Over the course of a long-term project, companies tend to neglect the importance of conducting proper data management, causing a higher rate of inaccurate data results and worse final predictions. With dozens of software developers teaming up to achieve bigger goals, the proper way of conducting data management for bigger projects is of absolute importance.