Behind all the technical aspects surrounding and constituting business data analytics as a whole, the goal of it all can be defined in rather simpler terms.
The idea is to use the analytical results for taking guided steps that would ultimately improve the business in some way or the other.
That is, in short, also the definition of business evolution as well. By analyzing past results every year, a company is able to improve and grow by being better equipped for maximizing the coming year.
Not taking anything away from the management’s credit, it is easier to make decisions when you are already presented with several detailed reports about existing/potential problems and possible solutions, complete with predictive analysis of outcomes that can come from deciding on each of the suggested solutions.
Unfortunately for business analysts, it’s never that simple. They do have to be familiar with all the nitty-gritty of data science and complete all the hard work that is required to generate those reports.
If you are a working business analyst already or even aspiring to become one, you may find the following tips and techniques to be quite useful in improving your own performance and time efficiency.
Familiarize Yourself with the Common Database Languages
Whether you have the title of a data analyst or a business analyst, you will inevitably be in constant interaction with the database.
A database in this respect is the same as the database management system on which it is built because that is the operating software that enables interaction with digital data, to begin with.
Now, it is neither impossible nor uncommon to find business analysts who do not use a database language to interact with their employer’s database management system. It can be done but it does require a lot of extra manual work, which eats into both the analyst’s, as well as the employer’s time every working day.
To avoid this, business analysts usually familiarize themselves with SQL at the very least, if not Python and R as well.
All three of these database programming languages are important and as an analyst, you should put an effort towards familiarizing yourself with them. In fact, if your employer is using a propriety database management software, it is possible that you will also need to learn their propriety database language during training.
As you can probably guess, that will be a lot easier if you are already familiar with the three main languages.
At the very least, you’ll need to get to know SQL well enough to use it effectively on a regular basis, or you will be spending far more hours in the office than your own productivity visualizations should suggest, unfortunately!
SQL or Structured Query Language is the top priority because almost every system out there supports analytical queries made in SQL.
For example, Oracle, MySQL, Microsoft SQL Server, and PostgreSQL are the most used database management applications across the world right now and all four of them respond splendidly to analysts who communicate with the systems by coding in SQL.
You will need a good understanding of SQL to decipher the database’s underlying design. After the initiation, you will be using your structured queries to locate, retrieve, extract, analyze and organize data from the database with the help of the language in no time.
Get to Know the Various Tools, How to Use Them and When
If you are already thinking about tools such as Zoho, Kafka, and Cassandra, then you are on the right track. Getting to all the three main languages is not as difficult as it sounds but mastering them is far from being easy.
Even if someone dedicates all their off time towards learning each language thoroughly, it will take them years to master all the different ways in which R and Python can be applied to solve the various problems a business analyst can face while doing their work.
On top of everything else, even that can become an extremely time-consuming chore if the data load is huge, compared to the number of available analysts for the job. This is where the tools of data analytics come in.
There is a long list of various software applications used by analysts to cut down their workload. Most of the digital age analytical tools work on the principle of automating the boring, repetitive work by setting the parameters properly for each operation.
As should be obvious, this requires advanced education and training in modern data science. In case you are currently working as a business analyst, complete your graduation in data science by getting a business analytics online degree that focuses on the various tools, languages, techniques, etc. used in cutting-edge predictive and prescriptive business analytics.
Go through your course’s curriculum first and then decide whether what they are offering to teach will be able to close your own knowledge gaps in data science.
Also, choose the online format if you are working because leaving a job for a full-time university may not be a viable financial option for most. Besides, learning while working will let you become more skilled and experienced at the same time.
Go Over the Standard Techniques Used in Business Analysis
Provided that you already have a background in business analytics, you should be familiar with most of the common techniques or principles used to create models. Nevertheless, you may not be familiar with the ones that your current/future employer insists on using.
Even if you are in charge, different use cases require different techniques, and your knowledge must be comprehensive to be able to ascertain which technique would work the best under which conditions. It is suggested by business analyst veterans who are still working in the sector that everyone should be familiar with the following.
Business Process Model (BPM)
Although not exclusive to the tech industry, IT companies do find the classic business process model to be quite adequate for most projects.
A BPM is created and used during the early stages of a project to determine and analyze all reasons and factors that separate the present business process from the target process.
This is done in three separate steps, followed by the fourth and final step which involves laying out all possible paths that can be taken to find viable solutions to each of the defined problems.
IT and business analysts usually subdivide these four steps and customize them to better suit their own specific needs. As for the steps themselves, check them out below.
- Strategizing, planning, and goal setting
- Describing and defining all initial conditions and standards for the new process
- Analytical comparison between the present and the target business process
- The final prescriptive analytics report which lays out possible solutions to the detailed issues
CATWOE is an analysis modeling technique that is based on determining and defining the six all-important factors relevant to any business process or a specific product:
- Customer: Recipients of the process output (stakeholder beneficiaries)
- Actor: Parties carrying out the business process (workers across the different job roles)
- Transformation: Interfaces of delivery/distribution of the process output
- Weltanschauung: The big picture/the underlying principle
- Owner: The decision-making authority for the process (Majority stakeholder)
- Environment: The rules, regulations, conditions, and limitations that affect, guide and/or restrict the process.
CATWOE is commonly used to determine probability scenarios in investment sectors and for better corporate decision-making since the analysis model helps to create predictive reports on how major steps are likely to impact the six main parties involved with the said process.
MOST stands for Mission, Objectives, Strategies, and Tactics and it is believed by many to be one of the best, most straightforward approaches to creating business analytics models.
It is a system used to determine the company’s present capabilities, ultimate goals, and the strategic steps that must be taken to make progress towards those goals. We have the four broad steps briefed below:
- Mission: The ultimate goal/the primary underlying principle/purpose
- Objectives: A list of smaller, gradual and interconnected goals that will lead towards the fulfilment of the ultimate goal or purpose
- Strategies: Possible plans and avenues that lead to the fulfilling of each smaller objective
- Tactics: Methods of implementing the strategies and plans
The 6 Thinking Hats Model for Brainstorming
Brainstorming is a necessary step for finding solutions to tough problems in business, but Dr. Edward De Bono has created a popular model to maximize that step too. While there are several others, the 6 Thinking Hats Model enjoys a high rate of success for complex, multidimensional problems. The project team is divided into six different teams, aka the 6 Thinking Hats.
- The White Hat team is tasked with finding solutions based on available data and logical conjecture.
- The Red Hat team finds solutions based on experiential knowledge, intuition, and emotional intelligence.
- The Yellow Hat team highlights the best aspects of the current scenario and the best possible outcomes.
- The Black Hat team highlights the worst aspects of the current situation and the worst possible outcomes.
- The Green Hat team seeks to find creative and innovative solutions with out of the box thinking.
- The Blue Hat team exercises process control by ensuring everything aligns with the purpose of the project.
If it feels like data scientists are fond of clever nicknames, then that is simply because the names serve another purpose entirely. The abbreviations simplify each process in easy-to-remember names with interconnected steps.
Even when it’s not an acronym, you should be able to recognize the pattern in which all processes bind their broad steps together in the perfect order.