We live in a world where we collectively generate over two quintillion bytes of data daily. This (big) data propels growth and innovation, if leveraged correctly. Big data is now used by every industry, from the banking sector, which generates unparalleled quantities of data, to the media and entertainment industry. What is the value of these insights and what are the values of strategic recommendations they bring?
It's no longer a secret that big data is a driving force behind many of the world's most successful technology companies. Consider this, data insight-driven business can take away over $1.5 trillion annually from their less-informed peers. As more businesses adopt digital transformation to retain, process, and extract value from their massive amounts of data, however, it is becoming more challenging to evaluate and collect data in the most effective way.
Leveraging Data Pipelines
That's where machine learning can help. Data is a boon for machine learning systems. The more data a system receives, the more it learns to function better for business decisions. However, decisions that take too long are costly. In a 2016 annual shareholder letter, Jeff Bezos talked about his approach to decision making- "Most decisions should probably be made with somewhere around 70 percent of the information you wish you had," Bezos wrote in the letter. "If you wait for 90 percent, in most cases, you're probably being slow." These words hold even today. Though data uplifts machine learning algorithms, managers and data scientists must reach a consensus on how much of the data pipelines they should utilize to bring value, drive revenue, and help develop more customized products for the target audience.
Driving RoI Behind Data Science
Douglas Pestana, Senior Data Scientist at Life Extension, explains in his podcast "How to monetize and productize Data Science" that CEOs want an ROI on data science. At a certain point, they're going to start questioning the investments undertaken by the data science teams in building ML models and the analytics behind them. That means the ML model of a data product needs to solve a problem, and it needs to make money. Thus before the executives and senior managers start to question their investments in data science, the tech-abled data science professionals need to show an ROI from data science or face a salary cut or, worse, a layoff. Douglas continues to explain that CEOs don't know what machine learning is. They don't know what any of those techniques do and what it's capable of- this is where the skills of data science professionals come to test- giving the C-Suite that vision and showing them the unparalleled capabilities of AI data models and ML algorithms.
Data science is a revenue-generating department, and there's no question about it. Data science these days is at par with the sales and finance teams. Data science and analytics set up within a company are thus indispensably responsible for revenue growth and cost savings.
Value of Insights and Strategic Recommendations
Investing in data science is a critical, strategic move for any corporation that uses current scientific methodologies, algorithms, procedures, and systems to extract knowledge from data and leverage it for its strategic decisions. Data science can be harnessed to identify and refine a target client base that goes a long way towards generating more revenue. In sales, specifically lead management, models can analyze past customers and score leads, resulting in greater sales efficiency. With this, enterprises can learn which solutions are best suited and can help them get an accurate outcome.
Data science insights have found takers in progressive companies like Amazon, making machine learning a core tenet of their operating philosophy and investing heavily in data science. Furthermore, data-centric service providers, such as Netflix, Uber, and Alibaba, excel at applying analytics to improve their operations, targeting their products and delivering an exceptional customer experience. Innovation in data analytics will transform how products are made and services are delivered. As customers, we've come to expect a high level of personalization from the businesses we deal with: Amazon and Netflix recommend purchases and viewing options based on historical data analytics. At the same time, supermarkets and department stores make shopping easier by grouping products based on our buying habits.
Conclusion
Data analytics offers a powerful way to manage costs and allocate resources more effectively. Need-based query resolutions can increase the quality of the customer feedback loop and aid the development of new solutions to complex problems.
To capitalize on data science, businesses need to find ways to embrace innovation and experimentation. We've only scratched the surface of possibilities. If businesses can unlock this untapped potential, not only would they make more intelligent decisions today, but they will be in a better place to plan strategically to address the changing needs of their customers in the future.
If you’d like to learn more about data science in the enterprise, join us for DSS Virtual at Transform World on July 12. Register now for free!
