The competition for domination in industries and services is tighter than ever. Massive corporations with billion and trillions of dollars in collective or individual capital are shaping the global markets. Since there is so much money at stake in successful businesses, every advantage is now leveraged through technology to maintain market share and exponential growth.
Data science is the latest technological innovation available for businesses to obtain a competitive edge. Data science extrapolates scientific deductions from large swaths of information to produce a tactical advantage. It essentially detects patterns and trends from nuanced data sources that humans would be unable to analyze without the advantage of semiconductors and artificial intelligence.
Data Science Essentials
The essential elements of data science require reliable data to analyze. As the saying goes in the world of computer processing, garbage in, garbage out. This data is typically acquired from financial accounting statements, consumer surveys, feedback, geographical data, professional studies, broader market data, and as much information that can be gathered on a particular subject.
As the data is gathered, it may be structured into libraries. Organization of data is the first step in analytics. Then the relevant analytic processes can begin in relation to the objective sought. This sometimes results in predictive modeling or educated opinions on how to obtain the objective in relation to the statistical knowledge provided.
What is the Value of Data Science to Businesses?
Businesses typically use data sciences for product development, streamlining workflow and productivity, evaluating credit risks, fraud detection, optimization, monitoring, managing advanced equipment, AI bot interfaces, employee performance analysis, energy efficiency, and a vast array of improved guidance on human management decisions.
How Does Incorporating Data Science into Your Business Work?
Data science is a general field of technology with unlimited applications. How data science works for a particular business depends on the nature of the business and how cost-effective it is to implement technology for advanced statistical guidance.
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Data science is so vast in its potential applications, that fiscal practicality must be the chief concern of any business considering it. It is most useful for large businesses that engage in high-volume activities that are perplexing to analyze and manage without technological assistance.
Smaller businesses can still take advantage of data science, however, by using data collection and artificial intelligence to streamline their customer service interfaces without investing in a large staff to handle general inquiries. A smaller business that deals with complex analytics of individuals, such as insurance companies, can also benefit from the complex algorithms and predictive modeling.
Data science adds objectivity to any complex field where professional opinions may be necessary. By using statistical data to calculate probabilities and guidance for the best course of action, it is easier to educate other executives on the proper management of a company and streamline it for growth, optimal maintenance, or whatever the objective may be.
A Data Analytics’ Approach
Data analytics typically takes five categorical approaches to develop business solutions:
- Trend Analysis to Improve Performance
- Statistical Support for Management Decisions
- Optimization of Products
- Optimization of Workflow and Energy Efficiency
- Predictive Modeling for Fiscal Management
A Few Examples of Solving Real Business Problems with Data Science
Trend Analysis to Improve Performance
This typically starts with measuring the satisfaction of customers, statistically organizing sales records, and gauging the efficiency of production. Computer algorithms can then detect patterns in the data and generate correlations between various factors to provide data science solutions on how to improve the business model.
This can be particularly valuable for businesses who are unsure of what price point will work for their product. Many businesses create a psychological value for their products by clever commercials, ads, and jingles. These psychological valuations can make use of the product entertaining even if it is lackluster.
Fast food chains usually rely on this type of marketing approach to sell inferior-quality products at exorbitant prices. Yet, this type of scheme can unravel if the prices become too high for a certain market segment or the quality falls below certain adequacy benchmarks. Data science can help franchises analyze the problem of price point evaluation to strike the perfect balance between profitability and customer satisfaction.
By analyzing the sales records and trends among competitors, inflation, consumer credit card debt, criminal classes, and other macroeconomic factors, franchises can individually determine what the ideal price point is for their particular location. Data science can consider all operational costs and factors to weigh the benefits of each price point.
Statistical Support for Management Decisions
This uses a similar process of gathering data on a subject within the management’s discretion and then providing advanced guidance by identifying trends and correlations. This is more focused on internal and external data related to even complex questions regarding, for example, ideal locations for beta testing a product or building a new storefront.
While a manager may have some ideas and a particular bias based on personal experiences or gut feelings, data science can provide objective reasons as to why one location is preferred over similarly situated locations. The data science may support its recommendation with data that similar stores were more successful when a myriad of factors were present.
The data science may, for example, determine that the business needs a lot of built-in foot traffic and would do best in a strip mall with large anchor stores. It may further determine that the ideal strip mall for the company is in a particular city because of general business growth factors conducive to the end goals. These factors may be demographics of the shoppers there, weather, low crime rates, and taxation laws favorable to the specific type of business.
Optimization of Products
The optimization of products generally relies on analyzing feedback, customer satisfaction surveys, and various trends. Optimization, however, is an ongoing process because fresh data must be obtained every time the product is tweaked or replaced to determine whether the guidance was flawed. AI guidance may be flawed if it fails to consider even one critical factor.
For example, Apple launches a new product and soon determines from customer feedback that users are not impressed with their latest Virtual Reality headset because of various reasons. The sales records show that a high volume are being returned after their launch. Apple had already used data services to justify investment in developing the product and assessing the popularity, profit potential, and market monopolization if they produce it.
Apple pinpoints several post-release factors using data collection to determine why so many headsets are being returned. Apple discovers that the headsets don’t fit every head size comfortably. They also learn that the VR headsets cause many users severe eye fatigue. Furthermore, they determine that others find the weight to be unbearable.
Apple is satisfied with its beta launch that their product still has great potential. However, the company reassesses the viability of its current model and determines potential areas for improvement. In this manner, Apples solves the VR headset problems in data science simulations elucidating how an adaptive strategy may be flawed.
It doesn’t make sense to make different sizes to fit every head because that would eat up more overhead in product development and infrastructure. Apple considers adaptable modeling of materials that can provide a more universal fit. Apple engages in problem solving data analysis focused specifically on eye fatigue to determine how many hours users can safely use the device to avoid this negative effect.
Conclusion
It is clear that data science consulting services provide businesses with a competitive edge. This edge may be the difference between success and failure.
Many large companies have gone bankrupt by failing to keep up with subtle trends emerging in the data that competitors caught onto faster. Any savvy business person should search for a reputable data science services company as soon as possible because money moves fast.
