CEO of Edge Platforms, EdgeVerve Systems Limited.
It’s common to start a new year with talk about technology trends and strategy. But amid economic uncertainties and recessionary pressures, leaders shouldn’t neglect near-term tactical opportunities to increase value. If you’re involved in IT and data leadership, now is a good time for a quick checkup.
How well are you handling the digital basics? Do you have a good understanding of all your processes? Are you confident in your approach to automation? Are you positioning data to leverage its maximum value? Is data moving at the right speed across your organization? If any of your answers present a cause for concern, please read on.
Getting the Components Right
Let’s reframe those questions as tasks. Executing any digital strategy depends on effectively digitizing all relevant data assets, including processes. Additionally, an effective framework for data automation is necessary, and data should be contextualized and its operational latency reduced to avoid leaving money on the table. Let’s look more closely at these essential components of almost any comprehensive digital strategy.
1. Digitize
One might think that we’re all done creating digital versions of analog data this far into the era of digital transformation. Yet that’s not the case. The usual processes and interactions between multiple stakeholders in a business create huge volumes of unstructured information on a continuous basis. Businesses are always working to convert this information into structured data as efficiently as possible using available tools—artificial intelligence (AI), natural language processing (NLP), etc. Once structured, data can then be manipulated, aggregated and analyzed.
When seeking to structure your data, there are a few avenues you can take. There are intelligent document-extraction technologies available that extract pertinent information from unstructured formats and make it accessible in digital formats. You can also focus on streamlining processes, enabling system communication and eliminating manual interventions to further facilitate data digitalization.
2. Discover
At the end of the day, continuous improvement and automation should be the aspiration of every enterprise. Achieving this requires understanding the processes and collaboration at work within your organization. Process discovery, also known as task mining, captures task-level data using user keystrokes and neural network algorithms in order to form a comprehensive process map. This map enables the identification of automation and improvement opportunities.
Challenges such as shadow IT, limited device access and large data volumes can make process discovery difficult. I recommend leveraging automation and AI tools that can help streamline your discovery processes, but effective discovery entails engaging users and subject matter experts as well as capturing task-level data to find and fill gaps so that those involved have a well-rounded understanding of each process.
3. Automate
With enough of the right data, you can begin deploying automated scripts that reduce the level of manual operations, thereby speeding up work while improving reliability and accuracy.
For instance, robotic process automation (RPA)—“a form of business process automation that allows anyone to define a set of instructions for a robot or ‘bot’ to perform”—is well underway on factory floors and in automated vehicles, with the support of AI and machine learning (ML). But I recommend that leaders welcome these new “digital workers” into the office, as well. With the labor shortages and limitations revealed during the pandemic, bots have had more opportunities to prove capable of closing books, offering price quotes, recovering payments, interacting with customers and executing a host of other repetitive tasks, with human workers supervising and intervening as needed.
4. Contextualize
To really reap the benefits of intelligent automation, we need the right data. Constant endeavors to digitize and discover allow us to harness data that drives contextual intelligence. This intelligence becomes more valuable as leaders integrate related systems within and between their organizations.
For instance, an F&B manufacturer needs contextual data from partner systems to effectively trace products in the supply chain. Similarly, an underwriter requires third-party data alongside insurance applications to price risks accurately. By making contextual information readily available, you can unlock the true potential of data for decision-making purposes.
The challenge lies in setting up the right governance between business and technology. As with automation, this sharing of policies and decision-making with “digital workers” may require a mindset shift. The goal is not to displace humans but to position data for maximum benefit.
To achieve greater contextualization, leaders need to invest in the right technology solutions. But that alone is not enough; it’s also important to drive awareness and usage of these systems in the enterprise and get people comfortable working alongside these digital workers.
5. Accelerate And Exchange
Data latency is a crucial aspect of efficiently delivering relevant data to the appropriate recipients. Poor data latency can cause delays in the transfer of information between departments or external partners, which may considerably affect various aspects of business operations such as sales, marketing and supply chains. It is also important to acknowledge that not all decisions necessitate real-time data and that there are costs involved in enhancing data speed.
To prevent these issues, consider conducting a thorough assessment to determine which functions, roles or decisions are sensitive to near-real-time data, and allocate your resources accordingly. Promoting collaborative insights is also vital for the success of an organization. One way to achieve this is by adopting a shared vocabulary with the other enterprises in your ecosystem to enable seamless information exchange.
A Practical Game Plan
Remember that digitizing and structuring data are prerequisites and process discovery is an important tool. Automation can augment your human workforce with new digital workers, so welcome them and situate your data in the right context to optimize how much these workers can accomplish. Finally, if it takes weeks or days—or even hours—to get critical data from one side of your business to the other, that’s a sign that you need to pick up the pace.
There is no one-and-done method to digital transformation. However, if an initiative is too abstract, it may have difficulty gaining traction. During this recessionary time, these few intermediate tasks can help keep you focused on value-accretive and cost-saving outcomes.
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