- It is about people: Data scientists love science, research, critical thinking, and getting the job done. There is an urgent need for dialogue between business and marketing professionals and data scientists — particularly when it comes to managing expectations, setting boundaries, and alignment when tackling a business challenge.
- Embrace data science: Most of those businesses were not paying attention and are now in the post Covid19 world having to find way, and budgets to quickly get up to speed in terms of digitizing every aspect of their business.
- Adapt or die: The business that fails to adapt quickly to the changing environment and impact of technology will not just raise the risk of complete failure the next time a global crisis occurs; it will effectively ensure its own competitive failure and, in a very short time, its inability to operate at all.
- Data driven: Every organization is already data driven, the question is what is practical and what makes common sense?
- Data science, if not now, then when? Irrespective of the industry you are in, digitization is no longer a ‘nice-to-have,’ it is an imperative. And it must happen now.
More data, more information
Business owners, managers, and decision makers are faced every day with more complex problems and more variables that need to be considered. In the current business environment, there is never a moment you will have less information — you will always have more.
More and more organizations these days use data as a decision support tool. The collection of knowledge and skills required by organizations to support these functions has been grouped under the term “data science.”
As the world becomes more and more data-driven, analytical skills are in high demand and data-driven is a hype. Organizations across the world want to work data-driven, want to manage their organizations in a data-driven manner, and want to develop their organizations effectiveness based on data. What does data-driven mean? And how can you establish a data-driven development in your organization? How can you, as a manager, decide on which data-driven initiatives are feasible for your organization?
What is data science?
When you look up Wikipedia, it says: “Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data.” For most people, this is still too complicated and confusing.
Data science consists of two words, “data” and “science.” Data relates to information; this can be structured data (for example sales data in a spreadsheet or rain precipitation data) or it can be unstructured data (for example Twitter feeds or news articles). Science indicates it requires a systematic approach towards gaining knowledge and insights through study or practice. It is at the intersection of mathematics, computer science, and domain expertise. Practitioners are called data scientists.
Data scientists develop algorithms that model and predict for example customer behavior, identify patterns, and trends based on collected data. What is interesting is to understand what can be done with the collected data, what can we learn from it. and how can we utilize it for our organization.
Data science and your organization
There is almost no organization that operates without any data, without legacy systems. The most basic companies have accounting, marketing, operational, technical, HR, and sales information, among other data.
As technology has advanced at warp speed, it has become common to use different information systems, which often are not interconnected. And it is the cross-section of the different department and domains that makes data science interesting. What could you do if you can use your internal data but connect it with external data?
A simple example would be an ice-cream business, which has sales data, inventory / stock data, but wants to predict which periods are the top periods to sell ice cream. Would it be useful to have meteorological data that can be included? What about doing a Facebook campaign if you want to target families with certain sets of demographics? And what if you want to do an ad campaign with Google Adwords? These are all examples of the application of data science. With data science, we want to put different information sources to work, so we may receive answers and accurate predictions to solve business challenges.
7 steps for discovery of data science
1. Understanding what data science is
2. Learning about your core data
3. Aligning company strategy and business objectives
4. Work towards a comprehensive DS roadmap
5. Define critical information to achieve business objectives
6. Select the right tools to support your roadmap
7. Integrate DS in your operational workflow
What we recently have done with success is to help our client’s management team understand the fundamentals of data science. This is to ensure that everyone is on the same page. Based on in-house workshops, we helped them get through the different concepts of data science. Once the understanding was established, we went through a process of understanding what kind of data is core for their business, what kind of information is kept, where, and how?
The next step is to understand the overall strategy of the company and the main business objectives. In the next phase, we start looking at how to develop a road map, where data science now becomes an integral part of business operations and helps to align application of data science with its business objectives.
Beyond plotting out the strategic road map, we helped with selecting the right tools for their organization, as well as advised them on how to incorporate different aspects of the data science workflow. Based on the requirements assessment, business strategy, and road map, we advised the client whether to look at standard off the shelf solutions or whether a tailor made solution was required. Predictive data analysis helped the business leaders to make better and more adequate decisions. We mention “predictive” as we expect that the algorithms provide us with a forward looking indicator based on the underlying data and patterns. The purpose is to make data science empower people and help business leaders make informed decisions backed by data.
Find below an example of a dashboard for an Indonesian wholesale business, which allows the sales executives to focus on their most profitable clients, taking into account the order frequencies, their order amounts, and ranking among the total field of company clients.
OML your trusted partner
Are you looking to make your first steps in data analytics, data science and need to transform your organization in embracing and increasingly competitive business environment? Are you looking for a professional advisor to accompany you on your data science journey? Please contact us TODAY for a free consultation and we will be happy to assist you. You can book your appointment by emailing [email protected].
About the author
Liang Tan is the founder of OML Consulting and has worked in the hospitality and technology management consulting industries for about two decades. He has provided consulting services to a variety of clients in different industries, ranging from technology start-ups to government institutions. He was a senior consultant for Cap Gemini Consulting. He is currently completing a Modular Masters in data science at the Singapore University of Technology and Design.