Execution of Change on the road to Desired Business Vision
I think the major trend which is changing the rules for businesses is digitalization. Companies are more inclined in the direction of a consumer-centric market and are trying to design its collaboration practices for better user experience. This opens up a couple of things, one among them is the emphasis on integration as companies are no longer relying on their proprietary data and analytics, they are leveraging data from other organizations as well.
The second thing is around data itself. Though data is not perceived to be a transactional asset, it’s always viewed as more of an analytic insight asset. A lot of companies actually leverage analytics as an insight within their data program but they forget the transactional assistance which again makes the insights a lot more challenging to find and identify.
The industry is also witnessing a trend—analyzing real-time data that is, in turn, driving the focus around data acquisition and analytics. So it’s about verifying that insights can be generated as and when events are actually occurring.
Also, there are a lot of repetitive tasks that can be enhanced by adopting machine learning, AI, automation robotics, and process automation. We are also looking at RTA which has been a critical service for us because, in manufacturing, there are a lot of repetitive tasks that we perform. So as long as we can produce the intelligence that is needed to go to the next step, we feel that there will be a lot more direct manufacturing. NRND is more about consistency, and so the automation of a process through technology assures that it guarantees the same quantity.
Last one that I can mention is about the IoT. We are leveraging IoT in the manufacturing process to ensure that we have fencers across our production line to guarantee a very high level of quality in our product. Also when we go through clinical trials, we need to collect information from the patient that includes measurements; capturing this information was really expensive about ten years ago. But now, as there is a reduction in the cost of consumer electronics, we can easily afford to capture them. We are introducing forensics IoT technology to verify that patients are taking our drugs in a frequency as prescribed by the doctor. Also, we can be proactive in refilling those drugs or ensure that there is no malfunctioning either in the effectiveness of the product or the devices’ software that is actually dispersing the blood.
Endorsing Collaborative Decision Making
The evolution of the enterprise architecture has shifted toward business partnerships and having conversations around solving problems that are not necessarily needed for a single project. The science of enterprise architecture is more around how we solve problems which are parallel to business units.
Without data, an individual is just another person with an opinion, and thus it’s important that we focus on data-driven solutions
We often can not only elaborate in terms of how technology can help solve these big problems, but we also understand the effort that is needed to stir from where we are today to where we need to be. So, the collaboration between business, IT, and enterprise architecture teams means that they have to be able to effectively speak the language of the business at the same time in order to bring technology innovation to the table.
Impact of Data-driven Market
Without data, an individual is just another person with an opinion, and thus it’s important that we focus on data-driven solutions. Within my team, we focus on corporate goals, objectives, and also KPI’s that have been defined as the measure to achieve these objectives. As enterprise architecture teams, what we tend to do is start creating solutions and initiatives that purely align with the most effective way to influence these KPIs. Considering portfolio projects where people are trying to align these projects and portfolios through their organizational goals, we take a top-down approach and measure the KPIs as to how they can impact the sales force effectiveness and how we can make the sales force more effective and then start to debug the methods.
Currently, we have defined our enterprise and data analytics strategy, and the discussions are indeed about the inside information that we need, to be most effective in and basically on moving our corporate rejected KPIs. We have started data visualization because we believe that what you can measure is what you can manage. So we are focusing on visualization of key performance indicators that are specifically tied to our corporate. After all, KPI is not just performance in general, but it is the performance of their utilization of human resources and other resources to make sure that the process is as efficient as it can be.
Piece of Advice
One advice is to acquire a deep understanding of the marketplace and business competition. Second is delivering on a proof of concept or having a quick win of how the architecture process can enable them to get to an innovative solution as quickly as they can. So it’s important in that case to not speak randomly but take an initiative that can give a sizeable example of value pretty quick. So I need to understand the business and talk the language of the business to form a partnership where a large problem can be sub-divided into smaller chunks and try to do a quick delivery of how technology can actually improve that process.