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Making the most of your data to drive effective business decisions

Making the most of your data to drive effective business decisions

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By focusing on value and not volume, organisations can start to find opportunities and efficiencies. Alessandro Chimera, Director of Digitalisation Strategy at TIBCO, offers insight into the true value of data and how business leaders can capitalise on their data to become more data-driven.

Alessandro Chimera, Director of Digitalisation Strategy at TIBCO

Soaring inflation, rising borrowing costs and cuts to public spending are never going to create an easy footing on which to maintain a business let alone grow one. For many organisations, difficult times are predicted and there is a need for urgency with inevitable fears of bankruptcy or insolvency. Understandably, decision-making needs to be quick and accurate. There has to be accountability but there also needs to be a clear direction and future planning. This is where data comes in.

While analysts widely agree on data’s intrinsic value and that all enterprises of any size must learn to capitalise on their available data to drive better business decisions, it’s not always that easy. Many enterprises face incredible data challenges: a flood of insignificant data, diluted data value, siloed data and inaccessible data. To gain the most from data, enterprises need to start by analysing the data journey across applications and systems. But that’s just the first step. Don’t just define yourself as data-driven — prove your ability to capitalise on data by becoming insight-driven.

Organisations need quality data and they need that data to be consistent and relevant. Given the scale of data generated currently, this can be a particularly difficult challenge. According to Statista, the global amount of data created, consumed and stored has reached 64.2 zettabytes in 2020 and by 2025, data creation is projected to reach 180 zettabytes.

It’s not just virtual meetings, social media, video streaming, wireless and mobile traffic, and broadband pipes driving this increase. It’s also the proliferation of much cheaper IoT devices.

Incredibly, just 2% of the new data created in 2020 was saved and retained into 2021 — the rest was either created or replicated primarily for consumption or temporarily cached and subsequently overwritten with newer data. 

To capitalise on data, organisations need to analyse historical data, or ‘data-at-rest’. They need to combine this with incoming real-time data, or ‘data-in-motion’, when its value is high and close to its creation. Advanced companies that can combine real-time data with historical data and use the latest data science capabilities have a strong competitive advantage. These organisations have fresh data that they can measure, put into context and create comparative analysis, to see trends, for example.

It’s the sort of set-up you get with predictive maintenance, where a model is trained on historical maintenance data that has recorded multiple breakdowns. Once the model is accurate enough, it is used with real-time data generated by the device to monitor equipment. When the model raises a possible anomaly, a notification is generated to alert the maintenance team, or an automated action is taken to remediate the equipment issue.

According to McKinsey, better insights into data can support growth or reduce costs in two key ways; firstly, through enabling better customer experiences, to increase sales and upsell, while also preventing churn and enabling stock optimisation. The second way is optimised processes, using capabilities, such as predictive maintenance, demand planning and supply chain planning and optimisation to realise real cost benefits.

Consistently delivering the right product at the right time, with service beyond expectations has become a universal goal, regardless of the industry. A good example of this is global railway manufacturer and management company, Siemens Mobility. The business was looking to use data insights to prevent delays due to equipment failure but also provide accurate insights to customers through its app, Railigent. The challenge was to deliver real-time analytics for both IoT and legacy data and quickly provide insights for relevant stakeholders. That led to data analysis at the Edge, feeding back live intelligent data on components and equipment on trains, which has sped up equipment maintenance and minimised delays.

The weakest link

How do we do it? How do we get the data house in order and achieve the sort of results that Siemens Mobility is now experiencing? It’s important to realise that data value isn’t linear across different types. Typically, high-value, real-time data should be instantly analysed and then stored in an aggregated form for historical analysis. The data journey across an enterprise isn’t linear either. Different types of data can arrive from multiple sources, such as flat files, IoT devices, process-generated data and user input data — all travelling through the organisation from system to system or application to application. Data can be transformed, copied, aggregated, cleansed, extracted, or loaded and stored in areas before being used — taking multiple paths across a company.

This needs managing, especially as it’s important to also maximise data value at every stage of the value chain. And while access to data must be governed for regulatory and compliance needs, it must be accessible to those who need it. A simplified equation is:

Governed Data x Analytics x Technologies x People x Process = Value Captured

This insight value chain is multiplicative; its total value is as good as the weakest link in the chain, with typical weak points in organisations being the analytics process and its people. Most organisations have a huge amount of data already available. The same applies to processes, since those are already defined and eventually need to be optimised. Organisations miss capturing value from analytics since most still focus on historical analysis, whereas increased value comes from analysing data in real-time. 

The second critical component is people. If users cannot read, write and communicate data in context, even the best available data is useless.

So, what are the three high-level strategies that leaders should know and act on now?

  1. Increase data quality: Review how data travels throughout the company. Multiple copies, data latency across multiple stages, low quality and uncertain actions influence trust in data and their use.
  2. Support data literacy: Everyone must be able to understand data; data literacy must be an organisation’s top priority to use the value of data in any decision-making. 
  3. Use a platform ecosystem: Favour platforms with capabilities able to scale and grow with your business. Make sure that all platform services are well orchestrated and are available as-a-Service, on any cloud, on-premises, or at the Edge. A well-defined ecosystem builds a smarter data journey.

A single, well-defined data value platform is key here. It can manage and maximise value at any stage, as long as it’s available everywhere — as-a-Service, on any cloud, on-premises, or on the Edge. The platform ecosystem must scale with business needs and provide multiple horizontal planes, like a data plane or a control plane, and deliver multiple user experiences to developers, DevOps and business technologists. Only then can organisations truly use their data intelligently, speed up high-value decision-making and confront the oncoming economic climate with confidence.

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