Thanks to the availability of innovative technologies such as artificial intelligence, machine learning, and robotic process automation, modern companies have access to a wealth of tools to expand their analytical functions and extract additional value from the data at their disposal. Key to getting this right, however, is managing data effectively and understanding the very important relationship between advanced analytics and data management.
In a recently published report I came across, TDWI examines the best practices required for using data management in an advanced analytics environment, to really reap the benefits. The points outlined in the report are so valuable, that I will be summarising and sharing views over a series of articles linked to this theme.
Fundamentally, and as many in the data space will know, it comes down to the premise that successful forms of advanced analytics require adequate data management. The reports sums this up perfectly: if a company puts ‘garbage’ in, it stands to reason that it will only get ‘garbage’ out irrespective of the technologies used.
Data management therefore requires the right data in the right format on the right platform to deliver value through advanced analytics.
Matching needs
This means matching a combination of data management platforms, and tools, to each individual use case for advanced analytics. Much like any business challenge, the organisation must adopt a unique focus for each analytical function and manage the data processes or requirements round this. There is simply no one size fits all approach in this regard.
For example, as the report highlights, the analytics required when cross-examining massive data volumes (think of statistics and data mining) typically see users deploying Hadoop or a cloud-based management system. However, analytical solutions can actually be on premise or in the cloud. As such, it is wholly reliant on the requirements, and which tools are best suited for the job. In saying that, as more organisations continue their digital transformation journeys, the expectation is that there will be a natural increase in cloud-base solutions due to the high-performance capabilities that these can leverage.
Concepts unpacked
To understand this practice or approach, we must decipher the concepts to see their relationship more clearly. Advanced analytics is a collection of multiple user practices and tool types supporting techniques for data mining, statistics, predictive analytics, data visualisation, and others. Analytics should therefore be seen as a grouping of several practices with each having its own focus, abilities, value proposition and performance characteristics.
For its part, data management focuses on a variety of product types, technologies, and user practices that contribute to the successful handling of data. According to TDWI, these can be divided into data integration and data platforms. The former is about capturing and repurposing data for applications while the latter is the location where data is stored and managed to be provisioned for applications.
Both these elements need to be adapted to meet the extensive requirements of advanced analytics. Each method needed for advanced analytics can require a combination of data integration and data platforms in order to deliver value effectively.
Seeing this relationship between a myriad of components emphasises the importance of data management for successful advanced analytics. Dubbed an emerging practice, it aims to raise the accuracy of analytics outcomes for the organisation by adapting data management practices to consider the unique needs of each analytics technique and solution. This entails the development of a targeted solution that is capable of truly analysing all aspects of the data without resulting in unaddressed needs.
So, join me next month as I explore how to overcome the barriers of data management for advanced analytics to harness the benefits on offer.
