Data Analytics
What is analytics?
It is a process of decision making and solving business problems by collection, manipulation and interpretation of data.
Analytics can be defined as, “the systematic computational analysis of data or statistics.”
Unstructured data is very difficult to work with. Both for humans and machines. Lack of structure makes it difficult for the compilation process as well. The most common confusion made by people is identifying the difference between data mining and data analytics. Data Mining is a process of retrieving necessary data from a pile of large raw data, whereas Data analytics strictly involves interpretation of data into meaningful outcome. It’s a step ahead of Data mining.
Big Data
Big data can be defined as,” extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.”
Big data definition changes from situation to situation and organisation to organisation
Advantages of Data Analytics
Data analytics can open door to unlimited opportunities for organisations at every level. Knowing the outcome is always better reacting to a outcome, this is possible by integrating big data with organisation. Good use of big data is guaranteed to improve overall performance of an organization by allowing them to:
– Improve operational processes by streamlining supply chains and increasing productivity.
– Detect fraud and flaws by keeping a close vigil.
– Refine financial processes by increasing visibility, providing insight and granting better control.
– Reduce risk by being predictive instead of being reactive to environment and change.
– Innovate and create new models for growth.
– Improve IT economics by increasing agility, flexibility and abilities of systems.
– Increase transparency of systems and processes in business.
– Reduce cost of managing systems and operations.
– Make quicker, cost-effective decisions.
– Create products and services based on insights.
Skills Required for Data Analysis
Companies and people, who already had identified the potential of big data are capitalizing on their investment. Still companies and people leveraging big data are making more capital than they ever made.
Despite considered the most demanding profession of decade, there is a huge lack of
trained professionals in the field of data analytics. Only 0.5% of data are analysed, besides only one third of bank data are analysed every day.
A profession which didn’t exist a decade ago is the most demanding profession today, any person with data analytics are background are in huge demand.
Big data is significantly different from traditional data, hence requires a specific and varied skill set to understand, analyse and interpret the given data. A data scientist must have skills from the combination of following domains:
- Computer Science
- Statistics
- Business Management
The skills a Data scientist must possess are as follows:
Computer Skills
– Programming Skills: Coding is one of the essential skills required to become a good data scientist for a simple reason being data science is still an evolving domain with no set parameters, hence it becomes necessary for an individual to learn the all aspects data science. The most common languages used in data science is Python and R.
– Technical Skills: Besides programming, data scientists have to be proficient in other technical programs and platforms like Hadoop, Hive, Spark, etc., that allow them to retrieve, manage, and analyse various forms of data from different sources.
Data Skills
– Warehousing Skills: Data warehousing is process involved in gathering data from different source. Data warehousing requires great analytics skills. It is one of the most shortage job.
– Quantitative & Statistical Skills: While technology is a key component of big data analysis, quantitative and statistical skills are essential are also important for data analytics.
– Analytical & Interpretation Skills: Data analytics obviously cannot be done without the know-how and knack to analyse and interpret data. Big data being bigger, more complex and unorganized than traditional data, requires strong analytical and interpretive skills.
Business Skills
Lastly, the data scientist must possess business skills to understand the business problem and implement in various aspects such as operations, finance, productivity, etc.
Types of Analytics:
| Types | Explanation | Examples |
|---|---|---|
| Descriptive | Descriptive analytics is summarizing raw data to provide a meaningful outcome | It is the most common type of analytics, used by organization for dashboard preparation and report making |
| Diagnostic | Diagnostic analytics is a successor to Descriptive analytics. Diagnostic analytical tools are used to find out the cause of the problem | In a business organization Descriptive and Diagnostic are used side by side |
| Predictive | Identify pattern in historic data and forecast trends in business | In healthcare domain, a person health can be predicted based on his/her past medical history |
| Prescriptive | Prescriptive analytics assist in identifying best option in any given situation. | A prescriptive analysis is what comes into play when your Uber driver gets the easier route from G maps. |
| Cognitive | Machine takes decision on its own based to outcome a solution. | Fraud detection, Self-driving cars, Chabot’s, Robots etc. |
The Future for Data Analysts
Data analysts are soaring worldwide. They are extremely popular among employers and job seekers alike. The increasing reliance on data for all business operations has made those capable of analysing data useful human resources. And their widespread demand has attracted more professionals than ever before to the booming field of data analytics.
“Data is useless without the skill to analyse it”, as Jeanne Harris (Senior Executive at Accenture Institute for High Performance) rightly pointed out. According to McKinsey Global, the US alone could face a shortage of 140,000 to 190,000 data analytics professionals. And a survey by Robert Half Technology revealed that more than half of the CIOs interviewed in their survey felt they were understaffed and not utilizing their data analytical potential.
Possible Careers
As the significance of data grows in the business world, there will be a corresponding increase in significance of professionals working in analytics. This growing significance is already reflected in the varied positions data analysts occupy in organizations presently. The trend, once again, indicates that already
popular: this list is still in the growing phase. Let us look, however, at some of the job profiles that are already popular:
- Data Analyst
- Analytics Consultant
- Business Analyst
- Analytics Manager
- Data Architect
- Metrics and Analytics Specialist
- Analytics Associate
These are only some of the job titles that data analysts acquire in business organizations. The list is presumably greater. Depending on responsibilities and abilities, data analysts can work under n number of job designations to perform several analytical functions.
Analytics Software
▪ Statistical & Data Analysis:
– Excel, R, Python, SAS, JMP, Alteryx, Matlab, Minitab, Statistica, Weka, Julia, KXEN, Win cross, @Risk, Knowledge Studio,
Angos, IBM Watson, Eviews, Enterprise Miner, SPSS, SPSS Modeler, SAP-HANA, Spark, Rapid miner etc.
▪ Data Visualization Tools:
– Excel, Tableau, Power BI, Qlikview, Spotfire, SAS VA, D3, Excelcius, JMP etc.
▪ Big Data Analytics Tools:
– Hadoop, Spark, MongoDB, Cassandra, HBase etc.
▪ Optimization Tools:
– R, Python, LINDO, CPLEX, GAMS, Evolver, SAS
▪ DBMS & ETL Tools:
– Oracle, DB/2, Teradata, SQL Server, Netezza, Aster, Informatica, Datastage, SAS ETL, AbInitio, SSIS, SAP Hana
▪ Business Intelligence Tools
– Business Objects, Cognos, Hyperion, OBI(Oracle Business Intelligence Suite), SAP Cloud Analytics etc.
▪ Cloud Computing Platforms
– Amazon Web Services (AWS), Micros Soft Azure, Google Cloud Platform (GCP) etc.