The key to improving business outcomes is knowing how to manage data. Organisations can invest in analytics platforms with the latest AI technology or invest in better data infrastructure. However, without proper data management, they will not realise the desired business outcomes. Data management is one of the most important processes a business can invest in. It is the key to improving operational efficiency and developing smarter, more effective business plans. In the past, we have talked about a data governance framework and its role in data management. In this blog post, we are going to discuss data profiling and data cataloguing for managing data.
When discussing ways to manage data, we need to take into account the two storage formats for data: Data warehouses and data lakes (more and more organisations are shifting to the latter), though they exist as a repository for data, how the data is collected and passed on for analysis is very different.
Data profiling helps organisations manage data better by categorising, naming and organising information. It involves running a diagnosis and examining data to check for inconsistencies between data categorisation and how it is labelled. Data profiling is a visual assessment that relies on business rules and analytical algorithms to check if data has the right format properly integrated into the system.
As the name implies, data cataloguing is the process of naming all the data elements found in the data lake. The idea is not to add an extra layer to conform the data but to manage data better by giving users the means to know and search for data elements stored in the data lake. Data cataloguing is not a new technique, but it has seen a resurgence due to the growing prominence of data lakes and the proliferation of automation technology. With data cataloguing, users are free to look at the data lake, no matter their technical expertise because vendors can create sophisticated tools that make the search process much easier than ever before. Cataloguing is particularly well-suited to manage data in data lakes because it makes the information accessible without compromising the open nature of data lakes.
Data profiling comes with several benefits organisations need to manage data better. The foremost benefit is the improvement in data quality, thanks to higher data consistency and more accurate readings. Profiling data makes it more credible because it eliminates any errors and accounts for missing values and outliers. It improves data management by centralising and organising company information. Moreover, data profiling has an immediate effect on business outcomes because it reveals surrounding trends, risks, opportunities, as well as, expose areas in the system that suffer from data quality issues, like input errors and data corruption.
Data lakes are very useful for streamlining data processing, governing data and developing new analytics models. However, continuously dumping data turns a data lake into a data swamp because adding data without criteria robs it of all clarity. Fortunately, data cataloguing can help categorise data in a data lake. Data catalogues help manage data a lot better due to its tagging system. It unites both structured and unstructured data through a common language with definitions, reports, metrics, models and dashboards. This unifying language is important for improving data management in a lake because it helps set relationships and associations between different data types, which could prove invaluable in the future. Besides, the unifying language allows non-technical professionals to understand the data in business terms.
Data catalogues help manage data because users can easily find what they are looking for with the catalogue. Essentially, a catalogue will allow users to find the precise data items they are looking for to make the analysis more efficient. Even better, data is more accessible because anyone within the organisation hierarchy can access the data they need. Furthermore, a catalogue improves trustworthiness within the organisation because it provides assurances that data is more accurate and reliable. Finally, cataloguing data makes the entire process of analysing more efficient than before because it makes finding data items much easier.
To profile data effectively, data analysts have to know about the three different methods of data profiling. The first type is relationship discovery, where analysts find connections, similarities, associations and differences between data sources. The other type of data profiling is structured discovery, where the focus is on formatting the data to make sure data in the warehouse is consistent across the board. This type of discovery uses basic statistical analysis to return information about the validity of the data. Finally, content discovery assesses the quality of data, by identifying incomplete, ambiguous and null values. Understanding the different data profiling methods is crucial to profile and manage data.
To start profiling data, it is gathered from multiple sources and the metadata is collected for analysis. Once data is collected and cleaned, profiling tools will be used to describe a dataset. The tools will evaluate the content to find existing relationships between value sets across data.
Of course, data profiling can be done in different ways to manage data: Column, cross-column and cross-table. Column profiling refers to the number of times a value appears in a column within each table, helping to uncover patterns within the data. Cross-column performs key and independent analysis to determine the relationships and dependencies within a table. Crosstable profiling determines which data can be mapped together and what might be redundant. The data is determined by finding the similarities and differences between syntax and data types in tables.
Automation plays a huge role in the creation of a data catalogue to manage data. However, creating a catalogue starts with accessing the metadata. Data catalogues use metadata to identify databases, data tables and files. The catalogue crawls through the company’s databases to bring the metadata to the data catalogue.
The second step to managing data with a data catalogue is to build a data dictionary, the dictionary contains descriptions, and detailed information on every table, file and metadata entities. Once the dictionary is complete, developers should profile the data to help users view the data quicker. The next step is marking the relationship – developers discover related data across multiple databases. Related data can be marked in different ways, like advanced algorithms and query logs from developers.
The next step is building a lineage, which can help trace data from its origin back to its destination. Data analysts will use this lineage to trace an error back to its cause. Then, the data needs to be extracted from the source and transferred to databases for cleansing. The process is known as ‘Extract, Transfer, Load’ or ETL. Once the data is loaded it should be arranged. Organising data can be done using several methods like tagging, automation and organising the specific usage. Machine learning (ML) models are integral to building a data catalogue because they can work with large data volumes. ML models can identify data types, relationships and incorporate information to increase accuracy. Machine learning models can help build a data catalogue at a faster rate and with greater accuracy, compared to more conventional methods.
Of course, it is important to keep in mind that there will be some challenges to profiling data and setting up data catalogues. For example, organisations have to account for unstructured data when setting up their catalogue and data profiling is very difficult to do – especially if there is a large volume of data to work with or if legacy systems are used. However, regardless of the challenges, there is no denying that profiling and cataloguing are two of the best ways to manage data. With proper profiling and cataloguing, the process of collecting and analysing information is made more efficient and easier to manage.
When organisations properly manage data, they have a clearer picture of the type of data they have in store, which gives them a better understanding of their strengths and weaknesses. Properly organised data improves the rate at which insights are generated because only the most relevant can be parsed for analysis. Irrelevant data will just muddy the results. Furthermore, the entire process will be more efficient because data is properly organised, improving operational efficiency.
Don’t just focus on data!
While organisations should take care to manage data with a comprehensive data framework. They should also optimise their data analytics platforms to improve the quality of findings and reduce overhead administration costs. Working with analytics experts and specialists can help organisations cut costs because they do not have to shoulder the technical and administrative burden of installing, administering and hosting analytics platforms. Analytics specialists can also find ways to optimise the analytics platforms to make it function more efficiently than before, in a manner that is more tailored to your requirements. Hence, organisations should manage data and invest in their data analytics environment to get the best outcomes.