The ultimate goal of Data Science is to answer difficult questions with data-driven insights. It is the amalgamation of various fields of science and computer technology to extract insights from data in any number of formats, structure or volume. Using Data Science, and its associated techniques such as Data Analytics and Data Engineering is no longer a matter of doing it or going without. To not do Data Science today is to get left behind to the dust. Most companies around the world are rushing to integrate Data Science into their processes, and those who do so successfully have become the brightest stars of their respective industries.
We are increasingly becoming an information-driven economy. Consumers and businesses in the United States are integrating technologies to drive efficiency and productivity
Source: www.seagate.com/files/www-content/our-story/trends/files/data-age-us-idc.pdf
Despite this advantage, some companies, specifically those who make the biggest decisions, are apprehensive of a Data Science buy-in. This is brought about by misconceptions and questions regarding value returns, optimization, skills and expertise needed to run a successful Data Science infrastructure. So how do you get your company to do Data Science and not just read about it? Here are some frequently asked questions by business stakeholders about data science and how we would answer them, to help you convince your company to make the jump and reap the numerous benefits of Data Science.
Questions like “How much will our sales improve?”, or “Will we get more value out of using this?” basically asks: Is our Data Science profitable? Knowing if and when your Data Science initiatives are successful is a matter of clearly detailing your goals and have a road-map to gauge your progress towards that goal. Setting-up definitive and precise metrics is one sure way to measure success. Improvements in a business process, such as engagements or lead conversions, can be done by marking a baseline: a stable point of performance before implementing Data Science. You can then compare succeeding results after implementing insights gained from data to this baseline.
Data Science adoption among businesses are growing worldwide. From merely 17% in 2015, 59% of companies are now using data to leverage business value
Source : www.forbes.com/sites/louiscolumbus/2018/12/23/big-data-analytics-adoption-soared-in-the-enterprise-in-2018/?sh=2421839332f4
An alternative means of measuring how your Data Science payoffs are through A/B case testing. It allows a particular sector within your business, to compare and contrast the results of different models. The beauty of A/B testing comes with its versatility to measure models regardless if they are already existing or inferred to yield better results. This gives a more coherent answer to the question above. Defining and measuring changes from a baseline or a comparative test can help you see if the modifications you are implementing are effective.
Deciding to do Data Science is one thing; implementing Data Science solutions to your business operations and other sectors can be a bigger challenge. You can have the best people in your Business Intelligence team but if they are not gathering impactful data or measuring the variables that matter, your Data Science will result in minor gains at best.
The value of data is increasing, and it is imperative for companies to understand the value of the data that they store
Source : www.forbes.com/sites/louiscolumbus/2018/12/23/big-data-analytics-adoption-soared-in-the-enterprise-in-2018/?sh=2421839332f4
You don’t have to commit to a complete overhaul of your operational paradigms or corporate culture because of Data Science. It is advisable to begin your forays into Data Science in Simple and small iterations once initial results start coming, preferably with the help of proven data experts. Having a new perspective from taking on Data Science partnerships can guide businesses towards greater internal collaboration in pursuit of objectives, such as sales targets. External support can also advance your Data Analytics maturity towards better alignment with your business goals.
Data Science is not magic. It does not claim to be an esoteric cure-all for any and every business dilemma. Good data experts know how to identify and select key areas that will benefit the most from Data Analytics. They also know how to follow this up by providing intuitive Data Visualizations and reports that depict performance indicators that matter. By tempering Data Science reports through periodic iterations in with meaningful visuals, data science experts can give valuable insights that will help make decision-makers create valuable solutions, without overwhelming them with information.
80% of most senior executives would like to buy-in to advanced Data Science initiatives lie AI and Machine Learning
Source : odsc.medium.com/what-you-need-to-know-about-adopting-big-data-ai-and-machine-learning-b19ea7d648b1
Data Science takes a lot of effort to pull off successfully. It will take a reasonable amount of time and expertise before getting results that matter. After allocating the requisite resources into Data Science, business stakeholders must then be briefed about realistic expectations when it comes to results. Thankfully, this is where competent data consultants are best at delivering. According to Mike Shane, president of FilAm Software Technology: "through the use of the right tools for each particular instance and with the right team of experts, partners like Ecuiti can cut-out time from data collection to value acquisition greatly." They are uniquely equipped with the people and experience to perform all levels of Data Science and its myriad processes. Delivering actionable insights that translates into greater value is what really sets these strategic business allies apart.
Do you have any more questions to add? Or do you have a Data Science buy-in story to share? Comment below and let us know.