Big data consultancy across the industries comes in many shapes and challenges. Though the majority of our client mission projects differ significantly from one another, our efficiency as consultants is in fact deeply rooted in centralised standardisation.
Several years ago, Intys developed a generic, rigorous and patented methodological approach to problem solving, called ISolv. This turned out to be a first-class idea. By circumventing countless hours of tedious, redundant work, ISolv has allowed our consultants to focus all their time and energy on project features that truly matter with regard to the end goals. As a result, higher-quality output results and (thereby) improved client satisfaction have repeatedly been observed. Being a big data analyst myself, a personal favourite feature of ISolv revolves around the typically dreaded process of raw data handling.
In an ideal situation, raw data objects (i.e. the data received/collected from our valued clients at the start of a project) would be perfectly formatted and ready to be plugged-in our various tool sets, functions and packages. In reality, however, this is rarely the case; these input objects require moderate to intensive handling before any kind of analyses may be conducted.
Be that as it may, though we maintain a high level of communication with our clients in the early stages of a project, we believe that the tedious data handling process that follows should not be left to them — not even partially. Our ISolv methodology was therefore designed to not only focus on achieving valuable output packages and insightful results, but to also systematically handle and process such raw data objects.
At Intys Consulting we are not only able to deliver valuable results and insights on all our projects, but also strive to operate on the "teach a man how to fish" philosophy as a means to empower our clients to conduct their own big data problem-solving analyses independently.
ISolv has been particularly useful in tackling a wide spectrum of data analysis challenges faced by our client companies. The use thereof has fore-mostly resulted in:
- Eliminating outdated databases with too many irrelevant data fields;
- Reducing long periods of time and effort to extract relevant information from raw data (e.g. managers being presented key business performance indicators too late to make efficient strategic decisions);
- Lowering frustration to keep dynamic (real-time) databases alive and running; and
- Saving time and money spent in training new employees to handle disorganised raw data sources.
You have big data project and need empowering data delivery? Then please do not hesitate to contact us!