11th Sep, 2018
Denave, Team, Designation
Data – the word considered to be a gold mine of information in the industrial landscape these days. However, important thing to note is that – data, in its raw form, isn’t going to give you much returns, it is only with intelligent use of BI tools, that one gets to mine the pot of gold. BI solutions have become the talk of the town and even without taking a step towards implementing effective BI solutions people are talking about it nevertheless.
BI solutions are aimed at helping you to harness the power of big data and analytics and eventually enable you to make more informed and swifter decisions.
Let’s look at the stages in your business with a comparative view of BI adoption and traditional processes:
The very first stage for any business process is Data Engineering (or Data Consolidation). It is not just the first stage, but also the most time consuming (almost 60% of entire process) stage as well. Let’s see, Data collation happens from multiple channels and it certainly comes in similar multitude of formats – the chaos can be imagined! And not to mention the time taken along with error possibilities involved in manual scrutiny and completion of the task.
With effective use of BI tools and a mix of database management and coding, the task of putting a sense into the interconnected yet unstructured data is achieved post which the relational data is stored in a Data Mart.
This is the stage where most companies lose sight of the goal and end up focusing only on the effort and task of managing the data. This stage provides the first set of inputs for the BI tools to convert data into information.
Moving on to the next stage, i.e.Visualisation, here, the element of human ‘expertise’ or intelligence has a decisive role to play. The task is more dependent upon the selection of right platform and the correct judgement regarding the structure selection for visualisation of the collated data. Some challenges which crop up frequently in this stage are:
- Non- interactive dashboards
- Dependence on IT and MIS teams
- Significant probability of delayed inputs resulting in loss of time
- Possibility of multiple disconnected call outs
Moving past these issues which are prevalent in pure play manual management, Data Visualization platforms can be leveraged to get quick and precise output leading to access to the data in a structured format providing the first uncluttered view of the data at hand.
Post visualisation is then the next step – Storytelling or Narratives, where the human discretion on what needs to be highlighted, holds extreme importance. In this stage, skewed or non-comprehensive connection of dots by stakeholders pose the biggest threat. There can be multiple reasons for it, such as, paucity of time at their end as well as, the limited overall understanding of the concept.
Comes in BI and you can have predefined dynamic story telling templates. Of course, the manual input is still involved but only at the time of defining the templates which is done in the beginning. So now, instead of just static, you get dynamic narratives.
Then comes in the stage of Analytics application
Often people mistake BI with just this stage while all the above-mentioned stages are critical to reach up to this level with the first stage, i.e. Data Engineering, holding the most significance.
This stage is all about the application of Analytics upon the insights to yield data backed action points. Nowadays machine learning plays an important role at this stage.
For Analytics stage too, there is human expertise required and the same may be in plenty when we look at the consulting conglomerates. The element of human expertise is critical at this point since without the right selection of analytics algorithm, you’re bound to get half-baked results from your insights, which will give non-substantiated predictions. Without the knowledge of apt statistical model to be used, again, the output is going to be vague and even incorrect at times.
With subject matter experts who understand the dynamics of BI tools and analytics, you can leverage the trend-based prediction modelling for a more informed state of affairs for your business.
While the comparative analysis of BI solutions usage and non-usage has been illustrated above, few major challenges make BI adoption all the more necessary:
- Lost opportunity cost due to human error and lax in the discretion.
- The cost of delayed decision-making.
Both the above cost can be avoided with timely investment in getting data management systems and process in place with an effective BI solution.
However, a BI solution if not implemented properly, will increase cost without significant returns. There is no magic wand here apart from an effective data engineering solution supported, BI platforms and Analytics practice.