Data Warehousing

A few years back, the industry thought leaders had put their heads together to deliberate on data generation and storage. But now, they talk about how to manage the collected data efficiently so as to make sense out of it.

It goes undisputed to say that data warehouses and data analytics have witnessed burgeoning growth within a short span of time. This growth has transformed us from an information craving generation to information overloaded populace.

Data collection and storage is no longer an issue. The real focus has now shifted on how to obtain business relevant data and make it comprehensible to promote decision-making.

Data Warehousing Conundrum

Data warehousing is not a luxury anymore that enterprises can choose to have. It is more of a bare necessity that drives business strategies. But when it comes to adopting the best warehousing architecture, we are still lost. The more you talk to people about it, the more number of opinions you get flooded with. The most common and germane advice dispensed is to link the warehousing structure to the data management function.

But the sad part is that enterprises view data warehousing and data management as two independent functions. A warehouse structure built on this faulty assumption will only yield non-relevant data or result in critical data getting lost.

Best Approach to Data Warehousing

A data warehouse solution can pack a host of technical capabilities. But that is not reason enough to justify its adoption for an enterprise. Experts opine that the best way to gauge the suitability of a warehouse structure is to evaluate the business value it delivers.

Enterprises today have multiple tiers of people that use the same data for various purposes at different levels. A data warehouse will deliver tangible business results only when these functions and operations are in alignment with each other. Thus, a solution that offers an integrated view of business relevant data at an enterprise level, and a bird’s eye view of all the people and processes using the data, will drive value.

Then, there is also the added option of cloud based data warehousing systems. The cloud platform is capable of providing scalable architecture to those enterprises that lack the resources to build an internal system. The online component of cloud imparts greater flexibility to enterprises to adopt a warehouse structure that can accommodate their future needs. Add to that the SaaS based warehousing solutions and you get a highly potent alternative.

Data Warehousing – A Failure?

The most common blunder that the enterprise IT guys make is that they think on the lines of ‘Data Warehousing or Business Intelligence’. It is not a question of ‘or’ but ‘and’. Data Warehousing and Business Intelligence should complement each other. Instead, enterprises end up investing in one initiative and ignore the other.

Unreasonable amount of data and unrealistic goals set by IT departments further challenge a data warehousing solution’s performance. The quality of data also plays a vital role in a data warehousing project. Lack of vision and commitment from the top management and budget constraints are other factors that sabotage data warehousing projects.

Data Warehousing – Critical Evaluation

Like any other concept, data warehousing too has its fair share of critics. They claim that data warehousing has only aggravated the issue of data management. According to them, data warehousing does not solve the issue of data quality – an underlying cause of information overload. Furthermore, data warehouses seldom come up with a single view of data across all enterprise levels.

But now with cloud based data warehouse structures, business critical data has breached the walls of enterprise parameters. Instead, this data now resides in platforms known for lack of security and transparency.

Wrap Up

It seems that there is always going to be a significant disagreement between the proposers and opponents of data warehouse systems. It is the enterprises who will have to make the final call. They need to bear in mind that data warehouses are not a one-time shot sort of things.