Hello friends, in today’s world everyone loves to have their own wall-mounted dashboards, but we rarely pick the right metrics to track.
Selecting the right metrics will offers at least two benefits
- Identifying how your business is doing. Are we going up / down / horizontal (on profit / loss).
- Identifying what to focus on. Predict the future; how to improve performance.
Today will focus on the Analytics, and its related terms: Why; What; When; How; Tools used in Analytics; and the Process.
In-short we can say that Analytics, through the systematic analysis of data or statistics, is turning raw data into insights for making better decisions.
Definition from Wikipedia:
Analytics is the discovery, interpretation, and communication of meaningful patterns in data.
Analytics is useful in areas which records a lot of data or information; thus, we can say that analytics relies on the application of statistics, IoT, computer programming, research, survey data collection etc.
Organizations will apply analytics to business data to gain useful information and insights, and based on this they predict the future, improve business performance, invest or not and make decisions.
Major areas within Analytics
- Data Analytics
- Descriptive Analytics
- Predictive Analytics
- Prescriptive Analytics
- Diagnostic Analytics
- Business Analytics
- Big Data Analytics
- Web Analytics
- Fraud / Risk Analytics
- And many more as you name it.
Before we dive deeper, lets understand the difference between Analytics and Analysis?
- Analysis is focused on understanding the past; what happened and why.
- Analytics is the science of analysis wherein we apply methods of statistics, data mining and computer technology for doing the analysis.
With the process of analyzing raw data to find trends and finding the answer to questions, the definition of data analytics captures its broad scope. However, it includes many techniques with many different goals.
In same line Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, developing insight and supporting decision-making.
As mentioned Data Analytics has 4 key components. By combining these 4 key components one can undertake data analytics and will be able to provide a clear picture of where you are and how your business is doing; where you have been; and where you should go (what to focus on).
Let’s examine the 4 key components.
- Descriptive analytics helps answer questions about what happened. It is the interpretation of historical data to better understand changes that have occurred in a business. Source: www.investopedia.com
These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs,) these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis and data visualization. This process provides essential insight into past performance.
- Diagnostic analytics helps answer questions about why things happened.
Here we take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they became better or worse.
To achieve this, we first identify the anomalies in the data. These are the unexpected changes in a metric or a market. Secondly the data that is related to these anomalies is collected. And finally, Statistical techniques are used to find relationships and trends that explain these anomalies.
- Predictive analytics helps to answer questions about what will happen in the future. Or tries to answer unforeseen phenomena, what is likely to happen?
These techniques use historical data to identify trends and determine if they are likely to recur. Here comes the most amazing and demanding tool and technique machine learning.
- Prescriptive analytics helps answer questions about what should be done.
By using insights from predictive analytics, data-driven decisions can be made.
Which is done using AI (Artificial Intelligence).
Different forms of analytics may provide varying amounts of value to a business, they all have their place, but when combined will extract more value from your data.
In the market there are numerous data analytics tools available. Most of them are open source tools and are user-friendly.
Probably the most widely used Data Analysis tool is Microsoft Excel, used for data processing, visualization, and complex calculations. Excel is a powerful analytical tool for Data Science. Excel is there from ages, but there are very few who uses excel to its fullest.
Next comes Python which gives you ample of libraries for Data Analytics, and for various another tasks other important tools include: R; SAS; Apache Spark; QlikView; Splunk; and Tableau are also good for Data Analytics.