Question

Please write one 200-250 word paragraph:What are the key differences between “big data” and “analytics”? What are management challenges executives leading big data transition must address? Why? How can big data management challenges be addressed?

Answer 1

Difference between "Big data" and "Analytics"

Big data is the infrastructure that supports analytics. Analytics is applied mathematics. Analytics is also called data science.

Big data

Big data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big  or it moves too fast or it exceeds current processing capacity.

Spark, Elastic search ,hadoop , etc. are tools that were written manly to handle the enormous data demands at yahoo, google, and Facebook. those companies wrote that software. with assistance from academics at places like Stanford, and then gave it away as open source software.

these systems can run across a network (cluster) of low cost commodity PCs. they can process more data can even the largest mainframe because you can just add as many additional machines to the cluster as you wish.

Hadoop is not a database. it is a distributed file system, meaning you can use that to store data that spans multiple machines.

Analystics

Big data analytics refers to the strategy of analyzing large volumes of data, or big data. This big data is gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. The aim in analyzing all this data is to uncover patterns and connections that might otherwise be invisible, and that might provide valuable insights about the users who created it. Through this insight, businesses may be able to gain an edge over their rivals and make superior business decisions.

It is difficult to process medical and industrial data like this for several reasons. First, the statistician/mathematician (data scientist) does know know COBOL, SQL, and other mainframe programming languages and databases. Thus the data scientist has to wait for the programmer to load data into the mainframe. Conversely, the programmer has limited or no knowledge of applied mathematics. It would be better if the data scientist could program this him or herself.

Management challenges executives leading big data transition

1.Lack of Coherent Data Strategy

In order to benefit from Big Data and analytics investments, organizations should have a coherent data strategy. Implementing data analytics is more than technology and gathering as much data as you can and then try to generate insights from the data collected.

2.Using the wrong data

I am sure you have heard of the saying “Garbage In, Garbage Out”. The same applies when it comes to Big Data and analytics investments. Analytics has changed alongside Big Data: from descriptive analytics, through to diagnostic analytics, to predictive analytics and now prescriptive analytics.

Each of the above analytical tools serves a different purpose and for the analysis to be trustworthy, data used must be reliable and accurate. One of the challenges facing many Finance teams is that they lack an understanding of which type of data to use for a specific analysis.

Collecting data just for the sake of collecting is their priority.

3.Lack of Data Integration

This is mostly because of the disconnection between business and IT. Finance speaks the business language, forms a hypothesis or wishes to perform “what if” scenarios and simulations with the data in order to derive decision insights. On the other hand, IT speaks the technical language, and writes the reports.

4.Lack of leadership talent and skills

Instead of going for the traditional accountants, CFOs must acquire people with data analytics skills. For instance, data cleansing and normalization is a highly technical and time-consuming task.

A finance professional who traditionally trained as an accountant and never upskilled will struggle to view and combine, for instance, social media data with existing data sources for analysis. Even though this individual is lacking in technical skills, he or she might be strong in business partnering.

An individual who is strong in business can complement one who is strong in technical skills. Both attributes are necessary for achieving breakthrough performance.

5.Aiming too high for success

Embracing data analytics and transforming finance into an analytics powerhouse is a journey. Unfortunately, many finance organizations aim too high without first developing an understanding of how to use analytics to improve business performance.

Starting with a pilot project will help you plan quick wins, take lessons and move forward.

It is therefore advisable to start small and celebrate small wins before embarking on a full-on implementation across the enterprise.