Once the business drivers are defined, we can start laying out the technology strategy and selecting the right technology solutions.
Digital world is changing rapidly, almost at the speed of light it seems. With the cost of technology dropping, so called computer-challenged public taking to digital gadgets at breath-neck speed, we are seeing a world of possibilities. Pressure on the business world to embrace technology is enormous. Challenge is to not just jump headfirst into the latest technology but to understand the business problem and implement the right technology in the optimal way may be even in phases. Executive leadership at the C-level must buy in and drive the process.
Understanding the Business Goals
It is critical to understand the business drivers or goals for the company which may need a technology intervention. It starts with a clear vision of the company. Those goals may be varied from increasing profitability, reducing costs, or improving efficiency. Each of those goals needs to be looked into deeply to find more actionable goals, which then can be addressed by technological changes. So, it is critical that the company leadership carefully analyzes the issues, and brainstorms ways to achieve those goals.
Increasing revenue, in turn, may mean expanding customer base, giving better customer experience, improving the quality of products and others. Reducing cost and increasing efficiency may involve improving supply chain, automating processes, reducing rework or improving quality. Process efficiency may involve a complex set of initiatives from replicating experts, automating the expert rules, initiating alerts and monitors, creating KPI measures, redesigning process flows and others.
In short, before jumping into solutions, it is imperative to know the most granular business drivers, each of them can be addressed separately. Once that is done, we can critically look at each of those and design technology and process solutions for them.
Defining the technology strategy
Once the business drivers are defined, we can start laying out the technology strategy and selecting the right technology solutions. Technology solutions can span a wide range of solutions: digital transformation, big data, analytics, internet of things, artificial intelligence and machine learning and other powerful technologies.
Big Data, Information Management
Significant amounts of data is available for almost every business domain from social media to financial data to manufacturing and healthcare. Data is being gathered by companies at a significant pace; clients are willing to share data; there is also a tremendous amount of data available in the public domain. The data landscape has changed tremendously – now, we have the data and also, it is much cheaper to store the data, especially in the cloud.
How we use that data is important. People’s preference on internet and social media interaction, for example, can help with the marketers determining what the fad is, where and when the merchandise will be of high demand and how to meet the demand from production to stocking the inventory in the last mile. Similarly, financial/lifestyle data can help determine the right investment and financial choices for the customers from insurance to investments to education and retirement goals.
What is critical here now is to properly protect the data and analyze how critical are various sets of data. Then, we must build systems to take advantage of the data – the trick is to work closely with the business and experts to validate the quality of the data first and then, determine how to use that data. Building systems to use the data is the next step.
Internet of Things
The potential of connecting a myriad of devices together, which can share information, can be triggered by other devices is huge. It allows user’s preferences to drive the usage; where manual operation is replaced by not only automated operation but more importantly, intelligent operation. The interflow of information is of huge significance as people’s preference on one device is very useful in making the right choices on the other. If a user likes watching sports, it impacts when to switch on TV, when to turn on the AC at his house for the important game, what sites to shop on for clothes and how to buy tickets. It makes not only the consumer experience greater – it enables the marketer to focus on the right things for the customer. Inter-connectivity and sharing of the right information is the key!
Artificial Intelligence, Machine Learning and Expert Systems
Tremendous potential exists in recreating superior levels of expertise, automating learning and driving expert level decision making. Challenge is not to over-engineer the solution but first get the right expert rules and implement it in the system. It involves several sessions getting the business rules out of the expert’s head, formulating them in more technical terms and then, implementing those in a system with the right triggers, which can be fired off the knowledge base.
Once the system is created, it is critical to analyze the areas where the rules are falling short and implementing the right machine learning solution. It may also result in a more manual approach where the expert analyzes the result variation in a properly drillable analytical tool and helps the system to tweak the parameters in the model. Machine learning and other sophisticated AI techniques come next, especially where the rules are complex, and the data set is huge. That may involve a more reiterative automated approach.
Analytics and Visualization
Days of static reporting and printing out reports on a printer are long gone. Users have become savvy – they want to see the latest snapshot live online (on a myriad of platforms from smartphones to laptops), where they can drill into the data and analyze the results. Giving the users the ability to analyze the data, determine what the problem is or how the model generating the data to be tweaked are critical components of the analysis. This ability also can help learn from errors and may be the precursor or the necessary first step before we build out a full blown machine learning solution. Experts are good at analyzing these results – we need to provide the data in a meaningful way with the ability for them to look below the hood and do the analysis.
Another critical area where we are seeing deployment of digital technology is in the area of process automation. It involves deep dive into the processes, determining the inefficient ones, redesigning the process flow. All this is process design work, which must happen before we start the process automation. Technology can be leveraged to alert and monitor processes, automatically start or shutdown processes, react to errors and stop errors affecting downstream processes. All these can significantly improve the process efficiency.
As we stated earlier, there are a myriad of technologies available to address business problems and deliver on business goals. It is important for the executive leadership to buy in and drive the process and analyze the business drivers first before embarking on technology implementation. Instead of going with the shiniest tool, we need to apply the right technology for the right business problem. We recommend defining the future state landscape, land on a roadmap before implementing solutions most probably in a phased manner.
Bottom-line: digital technology has unlocked a huge potential – it needs business acumen and technology expertise to define the vision, take the right approach and implement the solutions in a thoughtful manner.
The writer is a digital transformation and technology strategy leader with almost 30 years of experience in the United States. An IIT Computer scientist and gold medalist in the US, he went to the US on a NASA scholarship in 1987. He currently leads Data and Analytics for Shell Energy at Trading and Supply IT. He was head of strategy for Product Management at OpenLink Financial, the global leader for financial products and technology. He was Managing Director at Capco Energy Solutions. Partha has held technology leadership positions at many companies, including Schlumberger, El Paso Energy (Previously Tenneco), Accenture, Sapient among others. He compliments strategy and business vision with deep technical knowledge in enterprise architecture, digital transformation, data and analytics, AI and mathematical modeling, blockchain and emerging technologies. Partha brings strategic vision and leadership capabilities in driving solutions and roadmaps as technology executive. He has a master’s degree in computer science from the University of South Carolina and an MBA from Texas A&M University, specializing in Finance and Management Information Systems. Partha lives in Houston and enjoys sports and music with his wife, Arpita and children, Ishaan and Trisha.
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