Data is an integral part, if you accept this today then it will impact your life efficaciously. Earlier, data wasn’t that much important, it always left aside. But now every organization is taking data seriously. In this blog, I will decipher the usage of data in decision making through various flavours.
Decision-making ability is really a niche skill-headed stimulant in any workplace. I must tell you, every role within any organization, start-up, enterprise or a small scale business blow, decision making is always in mainstream.
You talk about any idea or a step to be taken towards that idea, people will ask you the numbers. Now, here numbers are not random, you have to be on top of the tip. Now, we are talking. So, whence we will do this? This is decision making through data where the chance of going wrong is not concerned.
We will be taking scenarios to decipher it in (further blogs).
Types & the way data stored
There are two types of data one is processed data & unprocessed data. Where processed data is valuable and used as a ladder in the regime of decision making.
Every raw data, that data generated through various sources like sensors, social media, transactional data- a bank or data transaction, machine data- generated through bots or automated system installed are called raw data.
Firstly, you need to understand the architecture behind data storage & data generation. There is an architecture behind every system which will capture the data being generated throughout the globe. Which means you enter something in a machine & it will get stored, now the way it stores data is important.
So, raw data is simply the stored data & processed data is the story comes out for any purpose. But in decision making, we will be talking about the same processed data.
Why data is important in decision making?
Now, decision making is simply understood by, examples. Suppose you work for a retail industry & new product is going to launch in a region.
>> Can we decide the behaviour of customers towards the new product?
>> Customer acceptance ratio of a new product?
>> Which distribution channel will be cost-effective?
>> Quantity to start with?
>> Type of campaigns to support the upscaling of sale?
>> Customer reach throughout the stores, eCommerce or direct selling?
>> Customer, retailers, wholesaler their feedback?
We can do this through transactions we have stored in databases & believe me, life after this would be easy, clear, programmable. Also, decision making throughout the processes of selling of goods & services would be effective, cost-effective, optimizable & lean.
Analytics in Decision Making
Well, analytics is really a whole different ball game in the show, I will frame a series of blogs for data analytics. But for now, a little excerpt is here:
The question, how analytics is helping in decision making with data which were never used in the first place?
Answer to this is really simple. Various hypothesis in analysis, which assist what, when & how should be done for a problem or loophole in a system.
- Descriptive Analytics: Deals with “What has happened”, around the problem. Will explore the current situation & state of system the developing the hypothesis. Here comes the RCA type approach. This type of analytics comes when you’re thinking widely, to solve the problem. You do a whole case study of problem or situation then find out the loophole.
- Diagnostics Analytics: Deals with “Why it has happened”, type of situation and try to find out the reason behind the problem occurrence. Here comes the RCA + Why-Why Analysis approach. This type of analytics comes when you dive deep into the situation to find the loophole.
- Predictive Analytics: Read data, analyze the pattern and behaviour then forwardly, what could happen on similar notes? Frankly, Identify potential target.
- Prescriptive Analytics: This is something doctors do with patients. “What should be done to mitigate the problem? Or Best possible action needs to be taken to mitigate a certain problem.” We do scenario analysis to reach the optimal solution.
- Adaptive & Autonomous Analytics: Analytics where we build autonomous system, machines or bots which are adaptive & continuous flawlessly. Once you feed the instruction, later they learn from behaviour. But, these systems built on the models from real-world data flow, and they are adaptive to any environment with a certain set of algorithms.
- Descriptive + Diagnostics analytics: Tools which can handle the manipulation of a large set of data & help visualize & interact with summarization.
- Predictive + Prescriptive Analytics: SAS, R, SPSS & Python. Etc.
- Optimization– Gurobi, Ilog, River Logic etc.
- Simulation: Venism, Anylogic, STELLA etc.
- Machine learning + NLP: Scikit, Tensorflow, Open NLP etc.
These tools can help in:
- Transforming the raw data.
- Improving the way non-standardize data is collected.
- Using analytics to help visualize the data.
- Tech us to standardize the collection of data across the globe in real-time.
- Generate analytics of data & produce reports.
- Bring life to the two-dimensional sheet.
Understanding the technology & its capabilities is mandatory these days, you can’t afford to lose the race. To understand the data & then thinking about it with this approach (data-driven) can help in making future decisions of any business precisely.