As the pace of digital transformation accelerates, the amount of data available to businesses is growing at an unprecedented rate. And this data can be analysed to produce valuable business insights that can boost profits for organisations of all sizes. However, unlocking the value of this data can be a challenge for companies that have not used data analytics before.
“If you’re not already thinking about it, now’s the time to begin exploring how you can move beyond traditional business intelligence (BI) to real-time analytics in order to boost efficiency, improve security and drive innovation – all of which ultimately will help make your business run better and your customers happier,” explains Bob Rogers, Chief Data Scientist for Big Data Solutions at Intel in a blog post.
It may be daunting for businesses taking their first steps in the world of analytics, but the initial process can be broken down into three basic steps:
1. Establish a data strategy
The key to any aspect of digital transformation is having a detailed strategy in place, and data analytics is no different. In order to form a plan, businesses first need to establish exactly what data they have access to, whether that’s social media engagement, customer service feedback or maintenance records. Organisations also need to work out who will need to use the data.
The strategy must also include plans on how the data will be captured, stored, and analysed. Data warehouses have traditionally been used to manage structured datasets, but for dealing with the vast amount of varied data now generated by companies, they are no longer cost efficient. That’s why organisations are turning to cloud and open source data storage to hold their data securely.
2. Decide what kind of analytics you need
The second step involves looking at what you want to find out and therefore what kind of data analytics you need. IT managers should speak to individual departments to find out what exact questions they want to answer in order to give them valuable insights that they can put into practice.
“If you’re not already thinking about it, now’s the time to begin exploring how you can move beyond traditional business intelligence (BI) to real-time analytics in order to boost efficiency, improve security, and drive innovation”
There are five general types of data analytics to choose from, depending on the kind of data held and the kind of questions that need to be answered. Descriptive analytics is the most basic form and simply tells you what has happened. Diagnostic analytics goes one step further, telling you what has happened and why.
As the name suggests, predictive analytics looks into the future, predicting what will happen, when and why, while prescriptive analytics involves analysis driven by simulations, to optimise decisions and suggest the best course of action.
Lastly, cognitive analytics is the most complex form, involving computer-based simulated thought and actions. This is the type of analytics that includes AI and machine learning. More complex forms of analytics require more sophisticated IT infrastructure in place. However, it’s possible to experiment with more basic analytics models on existing networks before securing investment to scale up.
3. Build a layered analytics infrastructure
The exact IT infrastructure you develop should be tailored according to the needs of your business, existing hardware, and software and the types of analytics you intend to use. Whatever your preference, the stack should be comprised of four basic complementary layers: the infrastructure layer, the data layer, the analytics layer, and the applications layer.
The infrastructure layer forms a basis for the stack, enabling you to obtain, store, and protect data and run open-source analytics applications. As you might expect, the data layer is where the raw data sits in databases and data hubs. It may also be home to real-time data streaming in from IoT devices.
Next, the analytics layer is where various tools are used to transform raw data into a usable form to be fed into analytics applications. This is where the applications layer comes in – software analyses the data to provide customised analytics for different users. These analytics can then be used to inform business decisions and streamline operations. What’s more, extra security measures and AI technology can be applied across the whole stack to boost data protection and enhance analytical insights.
In the digital era, data is one of the most valuable assets that any organisation has and this is true across all sectors, from healthcare and manufacturing to telecoms, retail and entertainment. Making effective use of that data to drive future business gives companies a major competitive advantage. For those firms that haven’t yet made use of data analytics, it can be a daunting area to move into. But breaking the process down into the three simple steps above is a great place to start.
*Other names and brands may be claimed as the property of others
For more information:
- Intel warns businesses to act now or face extinction as digital disruption hits unprecedented new levels
- Are you ready for digital transformation? The 8 questions all businesses need to answer
- Rise of the Citizen Data Scientist: How an Analytics-focused workforce could give you valuable business insights