Big Data: Analytical Software vs. Statisticians

There are a few choices available to businesses in the world of analytics: you can use powerful, modern predictive analytics software, you can employ an actual statistician, or you can use a blended approach with both software and statisticians. There are pros and cons to each method, and the one that is right for your business will not only depend on what type of business you have but also how you use data. 

Big data can be disruptive to the future of nearly any business. When looking to the :future of big data and business, consider these factors:

• Privacy Matters. Customer privacy and the protection of personal identifying information (PII) is critical to all data collection and analytics. It is more regulated than ever before with the passage of GDPR. 

• Disruption Will Happen. Data will disrupt your business. It may redefine your marketing persona, change how you see your competition, and will likely automate many of your processes. 

• Big Data Creates Ethical Concerns. This is already happening in the area of self-driving cars and connected vehicles. Other dilemmas about how much control machines should have over our everyday lives will likely continue.

• Big Data Must Be Applicable. Communication will be key to the use of data going forward, because data that is not applicable and actionable is essentially useless.

• The IoT Means More Data. With the internet of things, more data is created every single day. What data should you store, and what data can you use? The answer to these questions will lie in the world of analytics. 

With all this data out there, what is the real answer when it comes to analytics? Between statisticians and programs, which should your company choose? Here is a quick breakdown for you.

Predictive Analytics Software

It used to be difficult for a business to gather the data that they need, but that is no longer an issue. The issue is analytics, and one of the most common solutions is predictive analytics software. Predictive analytics empowers decision making in a number of ways, and affects each type of business differently. 

Retail:

• Informs marketing efforts

• Impacts inventory and stocking levels

Manufacturing:

• Controls inventory management

• Organizes production and distribution

Oil and Gas: 

• Impacts safety

• Predicts the need for equipment maintenance

Health Insurance:

• Detects and prevents fraud

• Identifies at-risk patients and recommends interventions

You get the idea. The truth is, although the data is available, and many companies even employ software solutions, they don’t get the full benefit from them. The reason for this is that many businesses struggle with confidence in customer insights from data and analytics. 

What advantages would they gain from using predictive analytics software to analyze data? Software can be used to analyze a huge volume of data extremely rapidly. For instance, Starbucks uses big data gathered from those customers who choose to use their app to determine popular drink flavours, promotions, and even where the best place to put their next new store should be. 

The more data that is analyzed, the more useful predictive analytics is. The use of software allows for a huge breadth of data to be gathered, stored, and quickly analyzed. 

The Role of the Statistician

What does a statistician do? The real difference between a statistician and a data scientist is a narrow one, but there is a difference. The applied statistician is essentially a data analyst, but they use more tools than just computers. They also use physical data like actuaries and the research of other scientists to analyze patterns and trends, usually that are specific to a certain industry. 

A data scientist, on the other hand, is more of a software coder who creates the models needed to enable predictive analytics software to come to useful conclusions. Their role is more of programmer and creator than a hands-on analyst. 

However, in many cases, a statistician can do more than simple software can. The reason is that they add a human touch to the data that allows them to do deeper analysis and sometimes make connections and correlations that software alone cannot. 

Statistics is about being prepared, and that preparedness comes from a thorough depth of understanding of the gathered data, and its application to planning. Statisticians can be found in business and industries such as healthcare, education and research. The manual processing of rich data still has a place in many applications. 

A Blended Approach to Analytics

For larger corporations, typically there is a blended approach to analytics. Statisticians gather data and make sure the the company has all of the information they need. They use data gathered through predictive analytics software, but combine it with other sources of data as well, and may even enter that data into models for automated analysis. The importance of data backups offered by analytics software also ensures that both the raw data and analysis results are not lost in the case of a technological failure or if a statistician leaves one company to work for another. 

The difference is that since they do not rely solely on computers, their analysis often yields new information. Without the software tools available, their jobs would take much longer. Therefore, a blended approach has the benefit of more efficient statistical analysis, but keeps the ability of statisticians to look beyond the numbers.

The biggest obstacle to this approach? The cost. To deploy predictive analytics software and employ statisticians both is beyond the affordability of many small businesses, and even some larger ones. Quite often a company will simply have to choose which approach is best for them. 

A blended analytics approach is ideal for a variety of reasons, but where does outsourcing come in? 

Outsourcing vs. Proprietary Analysis

To outsource or not to outsource is often the question. Most of the time, a predictive analytics software solution can be viewed technically, as outsourcing. You are using another company’s program to analyze and even gather data for you. This is a good solution for the most part, but there are some concerns. These include confidentiality and privacy. 

The other drawback is that the models that analyze the data, because they are not proprietary, may not be as precise as you would like them to be. Modifying an inquiry of the data will often solve this problem, but the data you need must be there to be queried at all times. 

Proprietary, or in-house analytics has the primary advantage of being absolutely customizable. You can as a company, gather the data you need, and analyze it with queries tailored to meet your specific needs. This does require more cost, at least initially, for software development or modification. 

What is right for you and your company? That depends on what kind of analysis you need, and how much control of your data and queries you want and can afford. In the end, as long as you are gathering and using big data, you still may be ahead of your competition. The better you use it, the bigger the advantages you will gain. 

Author’s Bio

Devin prides himself on being a jack of all trades; his career trajectory is more a zigzag than an obvious trend, just the way he likes it. You can follow him on Twitter.