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As businesses in almost all sectors engage with digital transformation, collaboration has emerged as an essential strategic element. The question of who organisations should partner with on this most important of journeys has become critical.
One of the most fundamental areas for collaboration is in the use of data for maximum gain. Collaboration must address not just the data currently in use, but also map out the future as an integral part of an organisation’s ability to innovate with data.
The healthcare industry, for example, is becoming increasingly data-driven, from drug research to managing reimbursement for treatment. The financial industry too, is employing data more intelligently to improve decisions and manage risk more effectively. Some supply chain and logistics organisations have also achieved dominance through efficient use of data. Many companies, however, have yet to make their first significant moves down the track to greater real-time visibility and insights, and enhanced efficiency.
Manufacturing stands to substantially improve performance through greater operational transparency, measuring against KPIs instantaneously. Companies can use real-time operational intelligence, based on analytics, for automated predictive maintenance to slash downtime. Even governments can employ digital transformation to improve public safety, upgrade efficiency for functions that citizens use, and to streamline communication between agencies. This drive towards ‘e-Government’ necessitates the breaking down of the siloed legacy information systems that are still prevalent.
A major benefit of digital transformation is greater resilience in the face of uncertainty. Digital transformation can, for example, provide the flexibility to support evolving and dynamic hybrid work patterns for large numbers of employees. How organisations use data and the tools available will vary between different business domains and the specific needs of the organisation.
Key elements of digital transformation include analytics, Internet of Things (IoT) devices, artificial intelligence (AI), and machine learning (ML). Analytics turn business data into actionable insights, by for example, revealing potential irregularities in financial behaviour for a bank. But before organisations create predictive models, they must access, transform and harmonise data from multiple sources.
One of these fast-growing sources comprises the billions of IoT devices already in operation, with the figure set to increase dramatically every year. There is a huge opportunity for new insights from IoT in a manufacturing context, healthcare, logistics, and for improving performance in the oil and gas industry.
This exponential growth in data creates its own problems, which is where AI and ML are invaluable. AI and ML can help find patterns in data, and assist in moving from unstructured to structured datasets ready for analytics platforms to use. However, the quality of the data fed into AI and ML is fundamental to the success of these systems.
Data often remains stuck in organisational silos. Some of this data may be in private databases, some in the public cloud, perhaps with multiple implementations. This is where a smart data fabric, a new architectural approach that speeds and simplifies access to data assets across the entire business, can overcome the disparities in corporate data from many sources. It works by embedding analytics, including data exploration, business intelligence, ML, and natural language processing, within the data fabric itself. Using this approach, organisations find it faster and easier to gain new insights and power intelligent predictive and prescriptive services and applications. The fabric does all this while leaving the original data repositories in place, negating the need for expensive transitions.
Partnerships are critical at this point. While a company will be an expert in its own domain it will not necessarily be expert in digitally transforming through analytics, IoT insights, AI/ML, and development of a smart data fabric. By choosing best-in-breed partners in each area, companies ensure they get the best out of their existing data resources and technology investments, and develop innovations through the digital transformation process.
Digital transformation dramatically improves efficiency, powers innovation and provides the necessary information to reveal new opportunities. For example, healthcare organisations can use existing and emerging data to provide predictive diagnosis, or to research causes of emerging illnesses and more effective therapies. Energy companies should use data from smart utility meters to balance supply with demand to high levels of efficiency, allowing customers to take advantage of lower rates at times of low usage, such as cheap overnight tariffs. Retailers can optimise the supply chain and improve customer experience by exploiting deeper knowledge of consumer behaviour and product flow.
The gains from digital transformation are potentially great for each organisation. But as more companies embark on the process, it has already become much more than a bonus on top of existing business, it is essential to remaining competitive. Companies that put off digital transformation face an uncertain future. It is for these reasons that organisations must collaborate on digital transformation, selecting the right channel to ensure as smooth and direct a route to greater efficiency and innovation as possible.
This post was sponsored by InterSystems.
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