In today's digital world, where data has become the lifeblood of enterprises, an increasing number of organisations are realising, as they seek to move up the intelligence value chain, that their previous information strategies are thoroughly inadequate in the modern environment. Modern companies simply cannot be run on spreadsheets anymore and official reporting data that touches many hands is quickly becoming a thing of the past.
Thus, the concept of the data warehouse has never been more necessary. After all, an effective data warehouse serves as the foundation from which to build meaningful business and analytical intelligence. However, despite the clear need for such an environment, many businesses find the idea of actually building a data warehouse too large to contemplate. Thus, they instead find themselves crafting their analytical solutions from spreadsheets and query tools.
However, the current focus on governance, risk and compliance in large corporate entities means executives simply have to be comfortable that any reports prepared by their teams for them to sign are accurate. This is not easily done when the information comes from spreadsheets that can easily be inaccurate, contain incomplete data sets, or be misinterpreted.
Enter the data warehouse concept, which is all about building a solution to integrate data from multiple sources into a single source that supports analytical reporting and data analysis. Just as with spreadsheets, a poorly designed data warehouse can easily lead to the company acquiring and using inaccurate source data that will inevitably have a negative effect on the business.
The challenge is that avoiding such inaccuracies means designing the data warehouse properly, a time-consuming and challenging endeavour. For one thing, it is critical to define the requirements necessary for proper data warehouse design. While executives may well have a high-level perception of what they want out of the warehouse, they don't always completely understand all the implications of these perceptions, making it difficult to define them adequately.
Such lack of definition results in miscommunication between the business users and the technicians building the warehouse, ultimately resulting in a solution that fails to deliver the results expected by the users. This leads directly to a need for fixes and improvements following initial delivery, and causes greatly increased development fees.
The other key issue is performance. Just as a car must be carefully designed from the outset to meet the purposes for which it is intended, but still offer buyers the option of upgrading it to meet individual performance needs, so a data warehouse must also be carefully designed to meet overall performance requirements. In particular, the initial overall design must be carefully thought through to provide a stable foundation from which to begin.
And it is here that Datapult comes into its own. This solution is a revolutionary enterprise data warehouse builder that significantly improves time-to-market by removing all custom programming, SQL coding and 'extract, transform and load' (ETL) package writing. Not only that, but, thanks to an innovative, programmatic approach, it is able to leverage the meta-data of the underlying source information and build a state-of-the-art data warehouse in mere days, instead of the more customary months.
Datapult significantly shifts the landscape here, since traditional data warehouse development is fraught with problems such as overspending in budget and time, not to mention the high maintenance costs involved in keeping a bespoke solution running smoothly. Datapult, on the other hand, cuts out the risk and the time to market and provides a trustworthy, lightweight solution in days instead of months.
More crucially, it is also real-time, which means a Datapult data warehouse is in sync to the minute, when compared with the traditional data warehouse, which is generally only loaded on a nightly basis. It achieves this real-time synchronisation by leveraging the powerful SQL Server 'change data capture' functionality, which provides event-based logging. Consequently, a Datapult database will never be more than a minute behind the production database.
In essence, Datapult can build a data warehouse rapidly that centralises the entirety of an organisation's business data, facilitating swift analytics and ensuring data-inspired insights for all business users. It must be remembered that it also improves time-to-market and curtails the costs involved when preparing data for analysis, mining, machine learning and reporting.
The solution delivers a dynamic warehouse that transforms data optimised for a heavy transactional system and transfers it to one better suited for analytic querying. This means that business users can query a read-only optimised database and return business intelligence-rich results for extremely large data sets, all delivered with incredibly fast response times.
One significant advantage Datapult offers over the classic data warehouse approach is that it eliminates the administration and management demands that come with the latter. Datapult systematically takes care of infrastructure, ETL and data governance, allowing businesses to use their data effectively, instead of merely managing it.
It should be obvious that the traditional relational database management systems, such as SQL Server, are not going anywhere any time soon and will continue to be the heart of the business most of the time. This is why Datapult is designed to use SQL Server as a source.
Moreover, even systems that make use of big data or other database engines require a central data warehouse within their ecosystem. The beauty of Datapult is that it can do the majority of the work in providing this central source of truth, all while helping to reduce time and cost, and with increased stability and trust.
It is all about changing the way the organisation perceives data warehousing. Datapult uses best practices to build a trustworthy, dynamic data warehouse in record time. Therefore, for those enterprises seeking to exploit the big data available to them, Datapult is the ideal solution to place at the heart of the company's business intelligence infrastructure.
Datapult can build a data warehouse that transforms data optimised for a heavy transactional system in a way that facilitates swift analytics and ensures data-inspired insights for all business users.
Datapult will be showcased as a solution to overcoming the challenges of migrating to a modern data warehousing architecture at the ITWeb BI & Analytics Summit in Johannesburg in March 2019.
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