If you have a data problem, you are not alone. Data is growing at speeds no one could have predicted ten years ago, but few companies have put a stake in the ground to control the flood, and those who have are reaping the benefits of more agile decision-making.
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Why? Because with a good foundation of data and a more acute understanding of customers, organisations will have the tools at hand to shift their business model and shape it around the needs of customers as well as identify gaps and inefficiencies in its supply chain.
The data flood
Critically organisations know they need data, realise they need the systems to process data, but remain ill-equipped to delve into that data, maintain the data’s integrity and quality, and turn that data into insights. To achieve all these factors, a business needs to shift its focus to filtering the data landscape and making it part of an organisational process while reducing the number of disparate analytics tools and siloed data real estate.
Until we start piecing different pockets of data together and merging them in a more centralised and cohesive framework, it is just going to remain idle and useless. If the pandemic has taught us anything, it is that agility and efficiencies are the primary sources of business success, and the key to understanding how to embrace this lies in your data.
Centralised data is vital
So how do we get from A to Z without being thrown off course? The best place to start is to look at your data landscape and identify where critical data enters your organisation and where it is produced. Once this is established, then you need to ascertain the quality of your data – bad data is worse than no data at all.
With a view of where data is coming from and its quality, you can start making decisions on how you want to engage with your data. We often suggest that clients take an organisational and process-driven approach to their data as this helps to improve discipline around where data is housed and the quality of the data. But if neither of these is attainable, there are tools out there to help you perform data quality and data segmentation on your existing environment.
It all comes back to filtering your data landscape to the most appropriate areas and applications.
Deriving analytics
When you have a handle on your data, you need to apply analytics to gain the insights required to transform your business. As mentioned, a lot of companies have a lot of tools that they use to analyse their data. Still, many of these are proprietary, bespoke to a software solution, or different departments have acquired various tools, and the results are working against each other.
We need to shift our thinking around data and analytics from being a tool in a toolbox to a mindset that defines it as a platform that centralises, unifies, connects, and then allows you to predict your data. The pandemic has created an urgency for data analysis, but if your data is in different stores, you are going to suffer from data gravity – where the time to insights from data is not viable.
By creating a hyper-converged analytics platform, you bring analytics closer to your data, no matter where it is sourced, be it your data lake, IoT devices, or remote worker’s machines. This then allows for better data management which, when in place, enables you to make use of tools such as machine learning and AI.
Insights drive innovation
With an analytics platform, you become the master of your data, and you give your data scientists and business analysts a centralised environment from which they can build analytics into applications at the source. This is key when we consider the need for real-time or near real-time insights.
This also negates the need for the creation of individual and independent models every time analytics needs to be performed. It is a new and terrifying concept for some, but it is less complex than it sounds as there are platforms in the market to support hyper-converged analytics and that doesn’t require you to throw out all your existing tools. Instead, they create a plug and play environment for your analytics software that allows you to “send” analytics to the applications and services that require them.
When in place – the benefits are shortening time to insights, getting a handle on the data flood, creating custom analytics, and the marriage of data management and data science.
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