Now technology has infiltrated our everyday lives and smartphones provide us with an ever increasing array of varying information, the internet has become a far more accessible commodity. While it has grown enormously over the past decade, the data is actually easier to navigate than ever before, with seemingly limitless content rapidly available to be digested worldwide in an instant.
This data growth is not limited to just the consumable online content. A shift in attitudes and the advances in data capture technology has resulted in businesses possessing more data than they are currently able to gain meaningful insight from – the ‘big data’ challenge.
While the technology advances that have led to widespread availability of robust storage technology aids consumers, it has developed a real issue for businesses: all of the data is stored but its variety, location and immense size leaves potential insight untapped. Data, after all, is just data without effective analysis.
The data warehousing concept, initially crafted to draw structured transactional data from a range of sources, has transformed the availability of these enormous, separated datasets for businesses, alleviating access issues that slow reporting and subsequent decision making.
As the influx of unstructured data continues to shape the data input, the data warehouse model must adapt to not only offer storage, but analysis of the information-rich assets that, through traditional analysis, remain unavailable for cross-examination alongside structured data.
Before any effective analysis takes place, however, data must first be properly loaded into the warehouse with robust, properly planned rules applied. “You are what you eat” is a mantra all data warehouses should live by, as poor data quality slows reporting and clouds the view of decision makers, negating its primary purpose.
As data is fed into a warehouse, it must first be ‘cleansed’. By allowing this level of control, cleansing rules can automatically flag data that is incorrect, improperly formatted and that does not meet strict field-specific criteria. ‘Bad’ data can therefore be avoided and subsequent reporting can then be based on good data confidence.
This level of analysis requires honing the input data through robust data quality rules. Due to data warehouses pulling information from a variety of sources, differing input methodology usually results in data sets that do not match, with little differences such as the spelling of names (‘John’ or ‘Jon’) effectively duplicating individuals. By having set data quality rules, a report can be generated that flags potential inconsistencies while future algorithms are refined to proactively correct issues as they arise.
Finally, data validation rules can be used to flag and track any records that do not meet a set criteria. By making this data available for reporting, the number of records that do not adhere to set standards can be bought to attention, pinpointing exactly which data source requires further planning to match the rest of the imported data.
Traditional data warehouses have rigid schemas that struggle to keep up with the constant increase in data and the emergence of new unstructured data sources. To combat this, newer technologies encourage proactive development of context-specific rules that greatly increase data quality whilst allowing access to the increasing number of datasets required to make proactive informed decisions.