Understanding Different Eras of Data Analytics

Back in the 1950’s and 1970’s, the role of information systems (IS) was to support operational functions within the organization. IS was primarily used in transaction applications, involving accounting transactions. The focus was on IS efficiency and IS effectiveness. With the advent of internet and as information technology (IT) advanced over time, the methods to assess the data and the amount that could be processed increased significantly. Data became more quickly accessible and could be processed at greater speeds. This assisted organizations and managers to report and run data more effectively. Information systems have helped organizations improve their decision making process and help managers reduce the risk involved.

Understanding Different Eras of Data Analytics

The way in which Data Analytics field has evolved in terms of capabilities and volume is of significance. Analytics has evolved and has been perceived differently across different periods in time or eras.

ANALYTICS 1.0 – THE ERA OF BUSINESS INTELLIGENCE AND TRADITIONAL ANALYTICS

The term ‘Business analytics’ has been in use since the 1950’s where decision making was considered to be central to a manager’s job. It was something that consumed most of their time. And in order to take better and wide ranged decisions, managers needed the right set of information.

This was a progressive time, as for the first time transaction data had been recorded, aggregated and analyzed. To facilitate this a team of internal analysts used to operate from a back office to provide managers with feedback and information analysis to help them make better decisions to run the organization on a day to day basis. New computing tools were used initially by companies large enough to justify this investment. And maximum of the time was invested in data gathering and creating data sets than in analyzing the data, and the process was slow often taking up to a few months. Data warehouses started being set up to query and report the structured data using business intelligent software’s.

This required recruitment of competencies to manage this data. Because of the long and slow analysis time. The primary form of analysis was done on past data and did not provide any explanation or prediction.

However, traditional models for decision making left out certain key factors. It ignored outside factors and assumed that the human decision makers knew all the permutations for making a decision.

ANALYTICS 2.0 – THE ERA OF ‘BIG DATA’

Organizations have started swimming in larger and ever-expanding sea of data. Where data is too voluminous and unstructured, which cannot be analyzed by traditional methods.  The term ‘big data’ entails data from a burgeoning number of sources. From clickstream data from the internet, social media profiles, online purchase history, video streaming data, call center voice data, data about biological research etc. big data companies, for example, google alone processes and analyses over 24,000 terabytes worth of data. What distinguishes ‘big data’ from small data or data used in analytics 1.0 era is that big data goes beyond internal operations or transaction systems. It processes data from external sources such as the internet, sensors and ad campaigns. Organizations leverage this information to base their decisions on to create new offerings. They capitalize on 3 factors:

  1. They pay attention to data flow.
  2. They rely on data scientists rather than data analysts.
  3. Moving analytics beyond IT operations and into processes such as inventory, tracking, and production information.

The decision to implement new offerings on the backbone of these factors help them in customer acquisition and retention.

Innovative technologies and big data firms had to be acquired and the requisite skills had to be mastered by organizations to implement this. Technologies like Hadoop and noSQL were used to process and store big data as the volume of data was plenty for a single server. And ‘Cloud’ has been a disruptive form of technology to deliver these big data capabilities and has also been a good source to capture data.  Big data can be used to mine existing, old and new data sources constantly for patterns and opportunities to provide predictive and prescriptive analytics. This process requires extensive storage and high processing power and a reconfigurable flexible grid, all of which is provided by cloud.

Data scientists are the key resources in this era to carry forward the decisions based on this form of analytics. These professionals understand analytics, IT, have sound mathematical and statistical skills. This coupled with their high business acumen and their ability to communicate with key decision makers puts them in a position to shape businesses. This is what separates them from traditional data analysts.

Examples of how companies use analytics in this era to decide how to create unique customer offerings include:

  1. Facebook tracks user’s clickstream activities, the people you follow, the pages you follow to make recommendations such as, people you may know, groups or pages you might want to join or like.
  2. Linked in matches your profile, work experience and skills to suggest People You May Know, Jobs You May Like, Groups You May Be Interested In, etc.

ANALYTICS 3.0 – THE ERA OF COMPETING ON DATA AND DATA ENRICHED SERVICES

When every big organization started noticing how Silicon Valley big data firms began to get more attention via targeted ads, and how they started investing big in analytics driven customer offerings. They decided to follow suit and this was the emergence of the analytics 3.0 era.

Every firm in every industry that sold assets, worked with customers, and were involved in shipment or delivery operations had data, which it could use to improve its operations.

Analytics 3.0 is when traditional analytics and big data were used together to improve an organizations overall efficiency. This trend can be attributed to diffusion of enterprise IT such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM) systems. These can capture and process vast amount of data as part of their operation, and these capabilities further extended by Business intelligent systems enable a wider set of data analytic tools to be applied to operational data. Furthermore, the sources and opportunities for data collection outside of operational systems such as Mobile phones, vehicles, factory automation systems, are routinely instrumented to generate streams of data on their activities, making “reality mining” a possibility.

These enterprise solutions with related technologies allow organizations and its key decision makers to leverage information in one part to improve performance across the firm as a whole.

This form of data driven decision making is what firms compete on nowadays. By developing products and services around information, it helps them increase profitability and increase customer traction while reducing operating costs. Descriptive, predictive and prescriptive forms of analytics were all used together in this era. The Internet of Things (IoT) industry is a consequence of the analytics 3.0 era. The purpose of IoT is to provide interconnectivity between various devices and is hosted in a private cloud, such as Amazon Web Services. Analyzing the data sent to/from amongst which is collected via sensors helps organizations observe various performance indicators which can support better decision making to calibrate and control the further working of the IoT devices. For example: A cloud platform that allows hospitals to record and analyze inventory data meticulously. Remote monitoring and support would help during the unavailability of the equipment having a control check by scheduling and restocking supplies would help to increase efficiency to serve patients.

Some examples of how companies use analytics in this era to make key business decisions:

  1. General electric. GE is becoming increasingly optimum in their services and providing assets which is a part of their manufacturing business. They use sensors that stream data from turbines, locomotives, medical imaging devices which helps them determine an efficient service interval for these devices.
  2. UPS has used analytics in the frontline of its operations. They track package movements of over 16.3 million packages on an average every day. The initiative ORION (On-Road integrated optimization and navigation) comprised of using telematic sensors in more than 46,000 delivery trucks which track various forms of metrics such as speed, direction, braking and drivetrain performance. This form of tracking helped them determine the best route and improve daily operation level efficiency, and ended up saving them 4.5 million gallons of fuel per year.
  3. Starbucks is another prominent user of analytics. Through its Loyalty Card program, Starbucks is able to amass individualized purchase data on millions of its customers. They used this information to predict what purchases and offers an individual customer would likely be interested in. They reach out to customers regarding these offers via mobile phones. This system drew customers to their stores more frequently, in turn increasing their volume of sales.
  4. Amazon uses analytics along with business intelligence to provide product recommendations and market products tailored to individual user activity, and it also uses analytics for logistical operations. Its emphasis and in-depth analysis enables Amazon’s massive supply chain to run smoothly. They extend their use of analytics to optimizing shipping routes and inventory allocation in warehouses. Analytics constitutes for practically every step of Amazon’s supply process.
  5. Uber is a technology company, that offers customers cabs from any location, without physically owning any cabs. They use GPS tracking to provide customer the location of their cab, the amount of time the trip will take, and the estimated fare for the trip.

The era of analytics 3.0 has given opportunities to many disruptive innovations to take place, which has replaced legacy industries that dominated for decades. Using this form of analytics has made business model innovation and decisions facilitating this crucial for organization’s to survive in today’s market. For example: Uber replaced legacy taxi services all around the globe. Netflix’s offerings made blockbuster go bankrupt.

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