5 Steps to Master Big Data and Predictive Analytics

Dream. Dare. Do – that is Suyati’s work principle in a nutshell.

Sep
14
2015
  • Author:
  • Sahana Rajan

5 steps to master big data and predective analytics

Stepping out of the virtual reality was followed by eFootprints in the form of data. Though the term has not been coined yet, the time is not far that “data population” would be a formal category in the domain of analytics. The era of Big Data has brought with it the landmarks of petabytes (1,024 terabytes) and exabytes (1,024 petabytes). Big Data refers to either the humungous amount of data present in structured and unstructured form which is ever-expanding or the technology required to be able to deal with data of this measure.

Once the Big Data is laid out, predictive analytics are the strategists who analyze them to deduce the ongoing trends in eMarket and form forecasts about the upcoming events. The forecasts are displayed in probabilities. Depending on the domain, sources and method of research is selected. SAS (Predictive Analytics Suite), Microsoft (Microsoft Dynamics CRM Analytics Foundation) and IBM (IBM SPSS Statistics) are some of the companies which provide solution for Big Data and Predictive Analytics. One can choose between proprietary and open source technologies for data mining and predictive analytics. The five basic elements of any predictive analytics model are:

  1. Storm-Forecasts From Dust-Data: Differentiate Between Relevant And Irrelevant Data

The overflow of data must be succeeded by the use of tools which will allow categorizing them according to their functions in your model. This is similar to building dams to control flow of water in unchartered lands. Let the dams of your data analytics tool group data according to the criteria meter you are using.

Bring analysis to the tables of frontline employees by using tools that make process of collection and classification of data easy. This will prevent loosening the taxing pressure of data on the data artisans alone and allow them to direct attention solely towards the task of analysis and deduction.

Once your data is categorized, the particles belonging to a certain data-group can be utilized to forecast and infer a trend.

  1. The Data-Spinal Column for Brain of Analytics: Use Predictive Algorithms than Blind Deductions

Carly Fiorina (former Executive, President and Chair of Hewlett-Packard Co), talking about data analytics remarks that the goal is to turn data into information and consequently, information into insight. The rise of knowledge from data occurs when well-trodden algorithms are put to work in the machinery of bytes. The ground for innovation in predictive analytics lies not in coming up with new formulas but in emerging with unprecedented forecasts. This is possible when the data is strategized and analyzed to furnish exactly the kind of trend forecasts the company needs.

  1. Spearhead the Data-Army: Position a Chief Data Officer

Once you have set in place a team of data-crazed persons, it is important to recruit a leader who can direct the center on basis of the predefined algorithms sprinkled with a dash of ingenuity. The Chief Data Officer (CDO) will be responsible for executing the Big Data Model depending on needs of enterprise. Over the years, the trend has emerged of the CDO directly reporting to the Chief Executive Officer in virtue of the vast role being played by data analysis in enterprise decision-making. The chosen one must have idea-mediation skills that would allow prioritizing data needs of the company along with specialty in big data solutions like MapReduce, Hadoop and HBase.

  1. Diagnosing the Stakes of Data-Move: Supply Chain Risk Management

The macro risk examination will pave way for analyzing data and create micro changes. Follow up a description of your supply chain with self-assessment of vulnerabilities, and lastly, the evaluation of implication and identification of actions. See the bigger picture and recognize how your supply chain fits into this. What would be the cost of your inability to extract knowledge from the data? While companies spend a whooping average of 8% (of net sales) on their administration, transportation, inventory carrier, customer service and warehouse costs, they still do not have a holistic data-record on the micro-functions. Deduce the impact that your logistics framework is having on your company and collect data on the basis of this. Remember that the starting point must be with the internal holdings (asserts) and secondarily, your association with customers, retailers and providers.

  1. The Anatomy of Your Analytic Model: Selecting a Core Platform

Gartner reports that through 2015, about 80% of Fortune 500 companies will be disabled from extracting benefits from big data. The platform you choose will determine the future success of your data models. The ideal platform would be a cloud-based one which will avoid the costs involved in building from the ground your supply chain management or in bringing together point solutions. The platform must not only be able to take into account the live data pouring in, it should be capable of digesting temporal and geospatial data without which a company could end up much like a passionate traveler on unmapped lands.

The model must accommodate the multi-supply chain lines of your company. Combined with the data analytics army, the platform will become a breeding ground for intelligent forecasts and reasonable movement towards long-term success. The choice of cloud platform like Amazon Web Services (AWS) will pool in additional security and processing horse-force onto your data. The network of cloud companies will allow the sharing of desired information to pool in resources and move towards a revolution where Big Data would evolve into an eon of data-centered virtual realities. The minions in our movies will correspond tablet-shaped data syncing together to create probabilities that will determine the course of life for companies.

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