Overcoming Common Mistakes in Big Data Analysis

Data analysis is prone to mistakes due to lack of adequate specialized skills and undefined business needs. How can IT fix the wrong market predictions? This question causes pressure to many entrepreneurs operating large and small firms. Identifying the common mistakes makes it easy to implement a new system.

Olap completes the multi-dimensional analysis of a company data and aids in avoiding common errors in business operations. Online analytical processing controls the business intelligence applications and provides the capacity for complex calculations, sophisticated data modeling, and trend analysis. The program supports business performance management, forecasting, budgeting, financial reporting, data warehouse reporting, and planning. It enables users to review figures in various dimensions for improved decision- making.

Relationship between Quality Data and Output

Data quality determines how efficient big data can help with risk assessment. Incomplete or insufficient details would drive scientists towards incorrect conclusions hence potential disastrous. The efficiency of big data on risk evaluation depends on accuracy, consistency, timeliness, completeness, and relevance.

Failure to observe any of the above factors, data analytics might fail to offer the required risk valuation for an enterprise. Cathy O’Neil, a Harvard mathematician, talks about the shallow and volatile information sets used by companies to assess risks. In her book, she explains how this move is leading to financial crisis.

Common Mistakes of Big Data Analysis and Solutions

Big data results in risk management experience the following seven biases and business people should consider the outline solutions for the errors.

1. Confirmation Bias

Refers to a situation where scientists take limited data to support a hypothesis that they feel is correct. They ignore other details that do not match with their feelings. The professionals base their decisions entirely on their knowledge and experience. They should accommodate opinions raised by other staffs regarding the subject matter.

2. Selection Bias

Data scientists pick statistics subjectively like surveys. The analyst drafts the questions to shape the answers they want to receive. They control the kind of details they want from the field. IT teams need to structure questions to give the respondents a chance to air their concerns.

3. Misinterpretation of Outliers

Outliers are different from standard data, and any misinterpretation leads to results skewing. The two are confusing especially to a new data collector. Outline the difference between the two to avoid confusing them to acquire facts from the sector.

4. Simpson’s Paradox

The Simpson’s paradox applies when groups of information point to one source. The trend can change if the data scientist combines the sources. Insist on having numerous sources for your statistics for comparison and accuracy purposes. Get materials from different entities that deal with the subject matter.

5. Overlooking the Confounding Variables

Confusing terms and clauses could vary the output immensely. In some cases, the analysts tend to assume the meaning of technical words and proceeds to make decisions. Different parties in the company would end up confused. Get a team of professional technicians to discuss a strategy before you implement it.

6. Assuming the Bell Curve

Market analysts assume the bell curve when aggregating outcomes. In case of non-normality, they will produce wrong results. Handle each case separately and avoid using the outputs of another condition to argue the current occurrence. Use the previous results as a reference for the current scenario.

7. Over-fitting

The complicated and noisy model is a major mistake data analysts commit. Choose a simple approach that will give you accurate outcomes depending on your operations.

The Federal Trade Commission cautioned businesses of risks associated with hidden biases. These misconceptions contribute to opportunity disparities and make commodities more expensive, hence raising frauds and data breaches. The advantages of big data in risk management outweigh potential risks with wrong details.