Great model. Problem is many if not most organizations have the specialized data science talent to turn data into actionable insights.
I am concerned about the lack of data science talent. Without proper data modeling and predictive analytics expertise the potential is significantly reduced.
Data analytics boosts productivity only when matched with more astute ways of interpreting and acting on new data.
There are five broad ways in which using big data can create value. First, big data can unlock significant value by making information transparent and usable at much higher frequency. Second, as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time. Third, big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Fourth, sophisticated analytics can substantially improve decision-making. Finally, big data can be used to improve the development of the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance (preventive measures that take place before a failure occurs or is even noticed).
Yes, there will be a shortage of talent necessary to take advantage of data. But the potential value in innovation and productivity is huge - it would be a big mistake to ignore data analytics. The solution is to invest in data science human capital and processes.
According to McKinsey, by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
Why spend big money on tech when we will be unable to gain actionable value from all this big data?
The solution is to hire and develop the right people. You could be at huge disadvantage if competitors have data analytics strategy but you do not. Three goals:
The first goal is put the right technology in place. Study business requirements and processes. Study all the data. Make distinctions in data and scenario planning regarding what type of information would lead to actionable insights, better decisions, resource allocation and productivity. Evaluate vendor offerings and select the right fit for your unique business.
The second goal is to hire and train / invest the talent. Coach them up. Put the right talent and technology in action.
The third goal is to create and structure workflows and incentives to optimize the use of data.
All companies need to take data analytics seriously.
In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information.
An example is what Wall-Mart did with IT and data analytics in the retail industry. They kicked serious tukis.
Another example is Goldman Sachs in finance. They had a better combination of tech and people to make better decisions.
It requires both the right tech and talent to make data analytics work.
Obtaining and analyzing data is incomplete. The trick is exploiting data for competitive advantage.
This is harder then we thought.
Yes, it is hard. But the payoff is huge - see Wall-Mart and Goldman Sachs.
I suggest massive trial and error experiments to see what works.
I also suggest hiring talent with experience in predictive analytics and data modeling to best exploit the data.
I stress importance of creating and structuring workflows and incentives to optimize the use of data.
Analysis must be presented in timely fashion so decisions are made that have a material impact on the productivity, profitability or efficiency of the organization.
Then you can exploit available data for competitive advantage.
For a good introduction to big data, see the WinterCorp Executive Report: http://www.oracle.com/us/corporate/analystreports/infrastructure/winter-big-data-1438533.pdf
The Report describes what big data really is and why big data is enabling new business opportunities. The Report then reviews requirements and describes the approach adopted by Oracle Corporation to provide its customers with enterprise class products for big data.
The usual big data characteristics are:
1. Volume: there is a lot of data to be analyzed and/or the analysis is extremely intense. Either way, a lot of hardware is needed;
2. Variety: the data is not organized into simple, regular patterns as in a table; rather text, images and highly varied structures—or structures unknown in advance—are typical;
3. Velocity: the data comes into the data management system rapidly and often requires quick analysis or decision making.
In addition, Oracle offers the idea that big data often has low value density. That is, most of the data in its originally received form may be of low value.
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