If data is the fuel for the Analytics engine, it is important to have a good handle on data to keep the engine running. Though data is available in abundance, currently only a fraction of it is useful to obtain actionable insights. Data tends to be sticky and also get stale unless you have processes to manage data through its entire lifecycle. TEG understands the need for an effective data strategy to be in place in order to convert data into actionable insights.

Through a well defined data strategy, our customers start looking at data as an asset. TEG recommends a comprehensive data strategy exercise encompassing: identification of key data assets, data ownership, data governance procedures, data availability, data integration, data sensitivity, data security, and data quality. We also recognize that one size does not fit all and hence we offer a customized data strategy that aligns with your business objectives. This is often a good start for most companies on their journey to becoming Analytics competitor.

The importance of data quality cannot be emphasized enough. When critical business decisions are based on data, there needs to be high confidence on the data integrity and quality. Data quality is judged by its accuracy, integrity, consistency, completeness, validity, timeliness and accessibility. Data quality requirements also vary across departments. Marketing can work with data that is at a macro level while the finance department typically requires data quality at the lowest level of granularity to account for every penny in the financial transactions and reporting whether it is P&L statement or the cash slow statement.

Data can exist in various forms: structured, semi-structured, and unstructured. For some companies, the amount of un-structured data is growing at a faster pace than the structured data. This is primarily due to the increasing value of data coming from emails, blogs, and handwritten notes. Data also tends to deteriorate over a period of time and starts to lose its value if it is not maintained. To maintain and enhance the value of your data, it requires governance on an ongoing basis. The data governance program is a combination of processes, technology, and training. TEG understands the nature of the data best, various tools/technologies for ETL, data cleansing, and data staging.

We provide services to help you integrate data from disparate sources, stage the data, and get it ready for reporting, analysis, and or business analytics.

By now most organizations have realized the power of BI to compete effectively in their business. However, companies are at different stages of their BI maturity. On one end of the spectrum, some companies have achieved a high level of BI maturity to not only understand what is happening in their business at any given point in time but they have deployed sophisticated analytical techniques to optimize their areas of strength and key differentiators to stay ahead of the curve. These companies are not sitting on their laurels of success but constantly evaluating newer and better techniques to stay ahead. The other end of the spectrum are a set of companies that have just started or are stagnant with a basic operational system that at the best provides them reports on what happened in their system.

Using the latest BI tools combined with data management processes and domain specialists, TEG helps businesses make better decisions. By providing historical, current and predictive view of the business operations, our customers are proactive in their decision making. Business Intelligence applications can be department specific or integral to business operations throughout the enterprise. We deliver these services in the client environment or using our hosted analytics platform.

TEG leverages various decision science techniques from disciplines of mathematics, statistics, operations research, and econometrics to deliver actionable insights. Some of the commonly deployed techniques include:

Profiling- In-depth knowledge of the customers and prospects is essential for having a competitive edge. This helps in improved targeting and product development. Profile analysis is an excellent way to get to know your customers or prospects. It involves measuring common characteristics within a population of interest. The analysis variables include demographic and business specific data.

Pattern and Trend Analysis- Sequential pattern analysis is used to discover temporal relationships among data items. In online transactions, sequential pattern analysis techniques can be implemented to determine the common characteristics of all clients that visited a particular page within a certain time period. Combining these results with information from traditional transactional databases, user-access patterns and future sales associated with specific site traversal patterns can be predicted. This process helps determine the optimal after-market purchase offerings for specific product groups and different customer segments.

Predictive Modeling- Predictive Modeling is used to project outcomes based on the existence of other available variables. It plays a significant role in acquisition, retention, cross-sell, reactivation and supporting marketing strategies. Modeling can also be used to support ad and site content personalization and to design and execute targeted promotions, offers and incentives based on preferences and interests.