Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.
Using SSIS, Quality DaBi can help you implement an appropriate information management foundation that can deliver integrated, accurate, and timely data across your organization. Our data management solutions help ensure you provide trusted data in the areas of business intelligence, data warehousing, data migration, and master data management. Our goal is to provide decision makers with accurate, relevant and actionable information, resulting in improved profitability, improved customer satisfaction and operation efficiency.
Utilizing the ETL features of SQL Server Integration Services we can work with you to make sure data is sourced correctly, ensure its quality, and provide the appropriate data architecture for effective reporting and analysis. Our consulting services address the following areas:
- Integration of data from multiple, heterogeneous data sources
- Data cleansing and profiling to improve data quality
- Master data management
- Metadata management to ensure data lineage and history
- Management of information throughout its lifecycle to enhance governance and compliance
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Data cleansing (or ‘data scrubbing’) is detecting and then correcting or removing corrupt or inaccurate records from a record set.
After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been caused by different data dictionary definitions of similar entities in different stores, or caused by user entry errors or data which was corrupted in transmission or storage. Preprocessing the data will also guarantee that it is unambiguous, correct, and complete.
The actual process of data cleansing may involve removing typos or validating and correcting values against a known list of entities. The validation may be strict such as rejecting any address that does not have a valid ZIP code or fuzzy such as correcting records that partially match existing, known records. Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at entry time, rather than on batches of data.
The main idea behind customer segmentation is to break your customer base into unique groups that share specific characteristics. Those characteristics can be based on demographics, for example age or gender. Segmentation allows companies to identify relevant but for example underserved groups or to target specific groups in a relevant way.
In general, segmentation can be achieved in different ways. An easy way is the usage of basic demographic splits, if you want to group your customers by a single demographic variable. More complex segmentation methods include more variables into the clustering process.
It’s good to think about relevant criteria for the segments, so that you can get a meaning out of the data. Besides the final design of the segmentation the first step is to think about which data (which data sources) needs to be analyzed and how the data will be gathered. Using Online Surveys is of course a good way to collect customer feedback, but also CRM data or Social Media data is relevant in more holistic approaches.
Because customer segmentation can be used for many purposes, for instance to adjust the pricing strategy or to improve a product based on the needs of a customer segment, it’s necessary to include different data sources into the analysis. In Social CRM approaches it’s important to understand your customers’ needs in a differentiated way, because one size doesn’t fits all. That’s why customer segmentation is one good way to understand your customer a little bit better.
Customer profiling aims to describe types (persona) of customers. These types then can be used as a tool to identify the best prospects based on certain attributes or to improve services. Normally, different information is combined in customer profiling, such as demographics, but also behavioral data or information on lifestyle. Customer profiling helps to understand customers and can be used to improve customer satisfaction.
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