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It has become something of a cliché that data is a natural resource essential to running a business. It must be harnessed and turned into insights and competitive advantage.
Companies are certainly wise to adopt a data-driven approach, particularly now, when technologies like artificial intelligence, cloud and automation need a solid data strategy to work effectively.
But where to begin? Many organizations aren't sure, especially because of the massive, ever-expanding quantity of data. By 2025, 463 exabytes of data will be created daily -- equal to nearly 213 million DVDs, according to the World Economic Forum. Then, there is the needle-in-a-haystack task of finding the right data, which is scattered inside and outside the enterprise.
Companies need a strategy. Here are four considerations to keep in mind.
1. Invest in data-driven partnerships
Connect with analytics experts to create an industry-led augmented intelligence strategy. Partnerships, especially ones that integrate technology and industry expertise, increase access to talent and allow the creation of a culture of industry-led augmented intelligence.
The balance between human expertise and the power of machines is vital. When these elements come together, they can solve some of the most challenging business problems and transform experiences for employees and customers.
Genpact, a professional services firm based in New York, worked with a global medical company that had to manage such large volumes of disparate data that analyzing sales performance, competitor activity and customer behavior became extremely difficult. Analysts were spending more than 80% of their time extracting and cleaning data for reporting, leaving little time to turn this data into insights. They often had to rely on manual spreadsheets, which led to inaccuracies. Working together, we developed self-service analytics for our client's commercial team so they can generate their own insights on day-to-day activities.
Among the many benefits realized, employee productivity has risen 98%.
2. Diagnose and improve outdated processes.
Create the foundation for a sustainable future of data-driven decisions.
Data-driven transformation isn't simply applying digital technologies to obsolete processes. Those processes must be identified and transformed. For example, if the invoicing process at a global retailer is slow and cumbersome, some may suggest introducing automation technologies to increase speed and volume. More likely, there are issues within the process that need to be resolved before automation is applied. In this instance, automation would create more problems than it would solve.
One excellent way to uncover process inefficiencies is by process mining, which identifies problems in a process by using algorithms to analyze data from underlying systems. When applied correctly, it, essentially, creates a digital twin of operations, highlighting processes that are working well and ones that need improvement.
But to achieve this level of insight, enterprises need augmented intelligence -- in this case, applying human judgment to process mining.
One financial services company Genpact worked with wanted to reduce the time it took to approve business loans. The company wanted to identify where delays were happening, why and, ultimately, create an action plan to reduce approval time.
Through process mining, we created a digital twin that identified wait times and bottlenecks across the process, quantified reworks and exceptions, and identified the factors influencing approval times. With these insights, the company was able to take steps such as streamlining document gathering and aligning working hours that reduced approval time from 13 days to two days.
3. Harness the power of the cloud
Collate, process and analyze data at scale. Cloud technology underpins digital transformation by enabling the analysis of vast amounts of data. A solid strategy built around the cloud allows enterprises to gather and analyze external and third-party data along with internal data.
Once that is done, the organization's capabilities can expand exponentially. The cloud can optimize data and analytics behind the scenes, creating powerful customer experiences such as the way Netflix, Amazon and Spotify adjust algorithms daily based on users' preferences.
In addition, the cloud can help tackle many other kinds of big, difficult problems. For example, a healthcare solutions company needed streamlined oversight of finance and supply chain data to cut reporting time, standardize performance metrics and improve decision-making.
The company utilized a cloud-based data engagement platform harmonizing data across different systems. The outcome is a single dashboard that gives employees access to predictive and prescriptive finance and supply chain insights.
4. Apply industry-specific expertise
The efficient and ethical management of data is growing in importance, especially for enterprises in highly regulated industries like financial services. Companies realize they must address security, compliance and potential data biases.
This effort requires a concerted alignment across business functions. It means creating a clear strategy for data ensuring ethical usage and regulatory compliance.
For example, a financial services firm that needs to become risk compliant might implement data automation capabilities to improve its data management and reporting systems. These products efficiently standardize data taxonomy, spot potential errors and improve the quality of reports -- all while reducing risk and overhead costs for the firm.
Taken together, these four digital transformation strategies connect processes, technology and talent. It's precisely the kind of integration needed to harness data and transform any business.