How analytics delivers

The understanding and usage of data analytics can either contribute significantly to effective business operations or it can be compared to valuable intelligence falling between the cracks and lost forever. The question is - how big a crack do you have in your organisation?

Data analytics is a combination of highly qualified statisticians, data preparation, enhanced variables and detailed methodologies, aligned with an enterprise's strategy. Analytics encompasses these factors and analyses historical events to offer insights that help predict the outcome of future events.

If we look at a financial institution, which uses credit scoring to predict the likelihood of an individual to default on a loan, a predictive model will be used to determine the risk outcome. Or in a sales process, a predictive model will be used to define a potential customer's probability of making a purchase at a specific time.

When developing predictive models, different facets will be looked at, but most statisticians will focus on differentiating the unique independent variables and analysing sample sets of data to look for correlations and patterns that can be useful for predicting required outcomes. The problem is that most focus on using financial metrics and transactions and little other data. It's a bit like saying: "lf the person has money, let's take a chance and offer them our product whether or not it meets their current needs." This approach explains the low response rates generally realised.

A critical aspect therefore is identifying what data is needed for effective prediction - and what data ends up actually being available and used to build the models. Most analysts can't fix data - therefore they do the next best thing statistically, which is dropping or avoiding poor quality data.

Data analytics touches nearly all aspects of a business and it's nearly impossible to achieve effective business intelligence without it. So it's ironic that data quality, which helps ensure effective and sustainable statistical models, has not been accorded the needed attention. Julian Ardagh, CEO of Effective Intelligence, said most companies ignore or minimise their data quality issue and don't believe there is a system out there that can "fix" poor data. While the concept of data quality is fairly simply, there are several components businesses will need to grasp in order to achieve the desired quality for their information, he elaborated.

InfoArchitect is a data quality and enrichment platform that offers advanced data cleansing, standardisation, matching, validation and verification. It improves analytics through enriched variables, match-coding and match keys created through the cleansing and validation engine. The advantages are immediately recognised with real-time validation for true, construct accuracy of data that supports richer segmentation and profiling. It also offers a visual data profiling and exploration tool that discovers data inconsistencies and inaccuracies.

Data quality offers data analytics improved modelling effectiveness. Data analytics offers businesses the insights that support improved revenue-generating business practices and cost-cutting methods.