4 Key drivers of digital transformation

1. Culture and people: creation of future-proof employees

The potential future workforce should be digitally integrated with market realities and the outside world so that all opportunities can be taken advantage of. Integration, cross-pollination and innovative thinking are all facets of tomorrow’s human resources as automation takes on worldly and transactional tasks. Skills move to automation and analytics, with fewer focus on siloed expertise but more on widely integrated skills. In other words, the modern day worker must be skilled in automation and analysis and thus be able to identify opportunities throughout his or her working environment. Automation is going to be the responsibility of everyone.

In an automated, computational environment, the ‘Kaizen’ philosophy of the past has been effectively reapplied, which enables employees to take control and employ digital techniques in order to provide more added value. Therefore, the goal is to encourage “right” thought and willingness to embrace digital change. In other words, the culture of automation, research and a seamless partnership must be established, applied and exploited.

It is easier to retrain new talents than by retraining existing talents that drives cultural change.  Most large companies continue to find it difficult to move from conventional business models focused on face-to – face meetings and no human interactions to a modern way. Customers want another seamless digital experience, but companies are have difficulty delivering it. While resistance is inevitable, proving the commercial value of adding a digital layer to current products is the best way to overcome it.  It is important to speak to businesses in a language they understand, which is revenue. Therefore, if we are able to show the data or results that illustrate the commercial value of digitalization, they will be assisted.

2. Reassessing the digital business ‘process’

The game’s name in digital operations is agility and transparency. What this means: The goal is to create a smooth and optimized system end-to-end, while ‘integration’ adds a great deal of benefit, instead of addressing and maximizing different process pieces. For this, the method as it is must be checked to make sure it is logical, Method Exploration and Process Extraction approaches are implemented to find bottlenecks by visual heat maps, and optimize what should be optimized. The main metric, though, is not just process performance but the convergence of data, processes and decision taking in general. This is true data and reliable interactions for empowered employees.

There therefore needs to be a redefinition of modern day process management. Lean thoughts have traditionally guided the digital interpretation, however, the risk is that the solution is too static when relying solely on final process perfection. If we agree that a confusing solution is the right one we will work on end-to-end optimization as well.  It just is the operation of existing processes and not an optimized version. We need to find the right balance in the sense of market needs as it brings more promise in digital innovation by changing systems as they occur.

3. Digital technology: the final facilitator

While the process makes digital transformation easier, culture drives it, and people do – technology is the critical facilitator. Optimized human resources and smarter processes will in the future be combined to create a powerful force for change with increasingly smart and competent technology. The digitally transformed company will use tools that go far beyond anything imaginable. Tools (such as software) which are capable of making the right decision will describe the future: cognitive technology emulation of human decision-taking by means of automated solutions, and machine learning (systems which teach), which marks a step beyond cognition, based on autonomous algorithms. Thus, solutions are constantly improved based on the data they are supplied.

The Internet of Things and sensor technology is a huge potential. The priorities at present are the crash of heavy data with data acquired via IoT and sensors and the use of deep learning skills, delivering more value than traditional process excellence or optimization initiatives can ever deliver. Cloud offers so much technology nowadays integrated with solutions for learning. The magic sauce offers valuable insights into businesses which then enables agents to reduce churns and improve sales on the front line. The successes of today are not only measured by pricing but also by the way we do business. The ground-breaking opportunity for these technologies is the decentralization of knowledge and a completely different way of thinking about the workforce.

4. Data: Which brings all together

When we find digitization going from the front or the company to the core or the back office, the real chance is for us to handle the physical supply chain. Whilst the front-end or client-side has traditionally been responsible for consumer service and the processing of customer data, in a digitalized business that is moving up the value chain, this does not work. All necessary information (i.e. data) must be gathered from all parties for the purposes of automation to operate directly. Data is critical in structured, interactive systems because, unlike humans who can view a given data point depending on their desires, computers are often relatively static. Data is under pressure, like never before, with the new emphasis on massive data involving artificial learning and scientific research. How to address that is the question?

Many companies are moving from a data-driven strategy, driven by people. However, it means that your data needs to be good enough to make decisions without the need for additional human input. The challenge is experienced in particular as laws offer a professional incentive. To determine if the new product or service is appropriate for a data based business in compliance with legislation, it needs adequate knowledge about its consumers. While data drives amidst these opportunities, it can also present a problem if it tries to be everything to everyone. The biggest lesson is to develop the key skills of data management, to strengthen them and to rely on the details available to support them in ensuring a more robust system.

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