With the Internet of Things (IoT) exploding at a phenomenal rate businesses and individuals will soon have access to data sent to them from trillions of devices, Each data set will offer some sort of insight into services and day to day to operations.
This sounds like a great idea. But with this massive surge of information data managers and companies need to be able to sort through it in order to create actionable insights. And this doesn’t only apply to enterprises but to smaller businesses as well.
According to Chris Larkins, business unit manager for Dell Enterprise at Tarsus Distribution, the first step in dealing with big data, as it commonly known, is to create a data culture, or to create a process of social practice in both the public and private sectors. This will need all employees and decision-makers to focus on the information conveyed by the existing data sets, and make decisions and changes according to the results. This is instead of leading the development of the company based on previous experiences learned in a certain field and not really knowing the outcome.
“Although big data is driven by technology, technology has a small part to do with it. Big data is all about answering business questions quickly and decisively and delivering value to clients based on that data,” he says.
He continues by citing the use of the widely used Dell Data Maturity Model. According to the model, many companies are merely data aware. “ This is the lowest level in the model in where an organisation treats its data as mere surpluses from its business operations, and doesn’t pay a second thought to it.”
As organisations build up their data capabilities with the help of companies like Tarsus Distribution and its partners such as Dell EMC, they progress through the model’s levels with the ultimate ideal of becoming a data driven company. “At this level, all company decisions are made from the data it collects, resulting in data driven insights, and ultimately giving them the competitive edge,” says Larkins.
Laying the Groundwork
Before a company can reach the data driven level a few steps need to be put in place, which is where Dell EMC’s expertise and products come to the fore.
A company first needs to set up an analytics team whose sole job is to capture, structure, analyse and pass the information gained on to the relative business departments who can then action these insights.
Structured and unstructured data needs to be collected from various business entities and securely stored. Filtering and collating can then begin with the help of machine learning and artificial intelligence.
However, before moving onto the final stage, data quality needs to be taken into account. “High levels of data quality are best achieved by implementing data governance between employees, processes and technology,” comments Larkins. Some companies may want to assign ‘data owners’ who have the expertise over the data they are analysing and who will only pass the important information further down the line. Additional tools like data glossaries and dictionaries may also be of help.
Once properly sorted and analysed, various data sets can be passed through to the next stage – the visualisation stage – from where actionable insights will be gained.
Take it Slow
Larkins suggest that companies, especially those with large amounts of data to sort through, use a phased approach. “A company may want to prioritise where it will benefit most from data analytics and start implementing a data culture there,” he says.
He emphasises that a company cannot rely solely on technology to transform it from data aware to fully data driven. “Employees need to be taken into account and they need to be properly educated with the new processes, features and programs that are in place.”
Tarsus Distribution has a range of Dell EMC Ready Solutions for Data Analytics. They are designed to provide an end-to-end portfolio of predesigned, integrated and validated tools for big data analytics.
“Consisting of high-performance Dell EMC infrastructure, these solutions will simplify deployment and operation of big data analytics projects. The are also calculated to lower costs and to ensure a strong return on investment, as well as being optimised for performance and scalability,” concludes Larkins.