Blockbuster Video had built an empire with their video rental shops in the 90’s. Thethen CEO of Blockbuster, John Antioco, was approached by Reed Hastings in 2000. Hastings wanted to sell the company he had built – a video mailing service – to Blockbuster for $50 million. Antioco could not imagine a world where the viewers did not have to walk into a video rental shop. He declined the offer. Reed Hastings’ company is called Netflix and it is a $130 billion business today falling in the same bracket as Disney and Comcast. Blockbuster have closed their doors.
Businesses today do not even need a keen foresight to realize the dire necessity of digitization. It is a proven fact that companies that have successfully digitized their processes work 5 times faster than the ones that have not. The recent turn of events relating to the pandemic has pushed traditional enterprises further towards the abyss. The choice, right now, is between adopting a complete digital framework and running out of business.
What is digitization?
Digitization of an enterprise refers simply to the conversion of all the information contained within and used by the enterprise into a digital format where the information is represented as bits or units of data.
Now, all this data can be crucial in terms of business process management, human capital management, enterprise resource planning, and most importantly decision making. Data is a powerful corporate asset that is capable of delivering immense value provided it is captured, nurtured, and utilized with strategy and vision. Businesses adopt data governance frameworks to maintain data quality throughout its lifecycle – from acquisition to archival. Data science comes into play when an enterprise tries to propel itself ahead of the curve with the help of data.
What is data science?
Data science is a multidisciplinary field of knowledge that studies and develops ways of extracting insights out of data with the help of a scientific methodology. It fuses the disciplines of computer science and statistics; formulates algorithms; develops data mining techniques, and whatnot. It is an umbrella term that covers all the efforts directed towards extracting the potential value of data. Data scientists are high in demand and so is a data science course from any reputable institute. In order to reap the fruits of data science an organization needs to be digitized end to end.
What is end to end or E2E digitization?
We have already mentioned the life cycle of data. Now, data would not have a life cycle if it were not for E2E digitization. It means digitizing every aspect of the business. From documenting the processes involved in product development, right down to the contact points with the customers, everything has to be digitized so that data can seamlessly flow throughout the organization. The organization needs to adopt a digital first approach and it has to start with the leader, likely the CEO of the company. The importance of leadership in digitization has ramped up the importance of positions like chief information officer. End to end Digital transformation is a time taking process as it takes a lot of adaptation on the employees’ part. It is necessary, nonetheless.
E2E digitization has a synergic relationship with applied data science. While end to end digitization allows the creation of seamless data pipelines that can power the data science models – predictive and prescriptive analytical models in most cases – the insights drawn through data science practices can improve business processes, making the E2E digitization more robust. Data science has become an important accessory to the whole movement of digital transformation, which leads us to the next section.
Recent Trends of Data Science in digitization of enterprises
Data science is a crucial element in the vision for data driven success. More than half enterprises which have invested in data science are still hard pushed to utilize its full potential. Nonetheless, the industry has come a long way. Data science is currently used for:
- Predictive pricing of products and services.
- Removing accidental anomalies from the supply chain.
- Augmenting customer relationship management with AI and machine learning.
- Improving decision making in general.
- Enhancing cyber security and fraud detection.
- Improving human capital management with more definitive analysis.
How data science alleviates various pain points in business support functions
End to end digitization means every aspect of a business is open to the possibilities of applied data science. Let us see how data science helps enterprises address the challenges involved in various functions.
Customer Relationship Management or CRM
The goal of CRM is simple – streamlining all interactions with current and potential customers with the help of technology. The problem with CRM is it has to be extremely agile in order to adapt to the ever changing needs of the customers. With data science on board, an enterprise can now run trend analysis and behavioral analysis, by using machine learning algorithms for pattern recognition. This can help an enterprise be proactive in terms of CRM, control churn and generate more leads.
Human Capital Management or HCM
Data science can be used to great effect to analyze the performance of a certain employee or a certain team. These records can remove the uncertainty involved in promotions, layoffs, and appraisals. This helps the enterprises put the right people at the right place. Predictive models can be used in the process of hiring a new employee.
Enterprise Resource Planning or ERP
Managing the resources at your disposal can be a more daunting task than it may seem on the outset. It is necessary to streamline the whole process of resource allocation and accounting on a single platform in order to maintain transparency and efficiency. Digital ERP supported by data science empowers an enterprise with scalability, agility, and adaptability.
Finance and accounting
This is in fact, a part of ERP but it deserves some focused attention. In every enterprise the accounting team works according to a chart of accounts, which the people involved in sales and marketing are often unaware of. This can create a disparity that can turn into a financial disaster for a business. Not only does data science streamline the accounting process it also detects anomalies before they can do any harm.
These are just some of the areas where data science boosts the process of digital transformation. There will surely be many more of these as the businesses become more technologically equipped, which they will have to be in order to survive.