Framework allows you to move through data analysis in an organized way.
This is the second part of my series which focus on Big Data Analytics, if this is your first time I recommend you read my first article here which will introduce you to the Big data analytics.
A framework is a real or conceptual structure intended to serve as a support or guide for the building of something that expands the structure into something useful. Example In computer systems, a framework is often a layered structure indicating what kind of programs can be built and how they would interrelate.
Why do we need a framework for data analytics?
In data analytics, the framework allows you to move through data analysis in an organized way. It provides you with a process to follow as you scrutinize the data with your teams to identify and solve problems.
Imagine having a data-focused project with your team and start working on that project. If you’re not using a framework, there’s a good chance that different people will use different approaches to solve the same problem. Having different approaches will make it difficult to make a decision at different stages of your project and can be difficult to trace it back.
The framework will allow you to focus on the business outcomes first and the actions and decisions that enable the outcomes. It helps you to focus on attention on what generates value first before examining all the data that are available or data that are not available that needs to be procured.
As a data scientist of a data analyst, you might ask yourself “what analytic techniques can I use and what tools can help me to analyze my data”?. There are four types of data analytics, and the tools used to help build analysis: Descriptive analytics, Diagnostic analytics, Predictive Analytics, and Prescriptive analytics.
The choice of an analytical approach based on what do you want to get or know from the data. This ranges from whether you want to identify a problem, propose a solution to solve problems, provide recommendations or actions that should be taken in the future.
This helps you understand the current state of affairs in an organization. It lets you look at what is happening today and what has happened in the past. This type of analytics typically provides summarized information to understand currently existing sales patterns or customer behavior, customer profitability, past competitor actions, etc.
Specific techniques might include simple box plots, histogram charts with means, minimums, and maximums. Plotting the data in quartiles or deciles across a number of different variables. Or computing statistical measures like mean, mode, standard deviation, etc.
Descriptive analytics is very powerful for understanding the current state of affairs and for developing the hypothesis to anticipate where business problems and opportunities may lie.
This provides the reasons for what happened in the past. This type of analytics typically tries to go deeper into a specific reason or hypotheses based on descriptive analytics.
While descriptive analytics cast a wide net to understand the breadth of the data, diagnostic analytics goes deep into the costs of issues.
Unlike descriptive or diagnostic analytics, predictive analytics is more forward-looking. Predictive analytics lets you envision what could happen in the future. This type of analytics can help the client answer questions like, what are my customers likely to do in the future? What are my competitors likely to do? What will the market look like? How will the future impact my product or service?.
Predictive analytics typically predicts what could happen based on the evidence we have seen.
1. Microsoft Azure Machine Learning x Udacity — Lesson 4 Notes
2. Fundamentals of AI, ML and Deep Learning for Product Managers
3. Roadmap to Data Science
4. Work on Artificial Intelligence Projects
(4) Prescriptive Analytics
This goes beyond providing recommendations to actually executing the actions or taking the decisions that are right for a particular situation. It does this by looking at what happened in the past, the present state and all the future possibilities.
Prescriptive analytics provides answers to the question, what steps or interventions need to be taken(what is the solution) to achieve the desired outcomes? Often the intervention might be an optimal solution given the circumstances. Or the best possible action given the uncertainty in the environment and the limited information available.
Prescriptive analytics is powerful in understanding the right actions needed today to address future possibilities and put an organization in the best possible position to take advantage of future conditions.
So how can a company/organization apply this technique to solve their business problems? Let us take a look at the following case study.
The following case study will help you understand how different organizations and companies can apply Data analytics framework to reach their decision and increase the number of customers and revenues.
This is not going to be a complete one to one mapping but nevertheless to the extent that those connections exist it will help frame for you how the different levels of the analytics framework work and how the business practices and business strategy is changing as you move up.
General Electric Company
It is a large global company, it was founded more than a hundred years ago(1892) and it does many many things. General Electric makes light bulbs, health care stuff, energy stuff, windmills, gas turbines, jet engines, transportation equipment, railroad equipment and so on.
The use of data brought evaluation to General Electric company, about how data from industrial internet or internet obtain sources, sensors and so on are changing business, changing strategies, changing business practices, changing the management of talent in this environment and just completely revolutionizing business in some sense. Let’s look at the evolution of General Electric over the past several decades.
During the 1980s General Electric was selling different products to its customers such as light bulbs, jet engines, windmills, and other related products. Also, they separately sell parts and services this means they would sell you a certain product you would use it until it needs repair either because of normal wear and tear or because it’s broken. And you would come back to GE and then GE would sell you parts and services to fix it. This was a transaction model during the time.
There’s not much in the space of relationships and it’s one of the transactions of sales and services.
Then in the 1990s till about the 2010s GE started moving to it’s more of a contractual model where they would guarantee the performance of their products. Example they will sell you a windmill, but GE will guarantee that the windmill work for 90% of the time over the course of one year. And they give you a condition under which it would work and they will tell you what they would do if the uptime is less than 90% and so on.
Now it’s no longer just a transactional relationship, where GE sells you the piece of equipment and then GE walks away. During this Model, GE has to engage with the client to work on operating, maintaining, doing preventative maintenance on the piece of equipment.
To be able to guarantee this level of performance it requires deeper engagement but also deeper analytics.
3.Expanded Customer Outcome Model
During this model, GE not only guaranteeing uptime but GE is going to help its clients make the best use of equipment that’s has been sold to them.
Example GE now says that well okay, we sold this equipment, we have sensors and devices on this equipment to figure out or to compute their operating parameters to get their operational details, from that we can do an analytics to then actually tell you(client) how best to operate all of the equipments to maximize the value.
So for instance if the client has bought a whole series of windmills, when to let the windmills run, when is the wind too high that you just have to let the windmills spin freely, when do you schedule the maintenance across the different units so that overall the power output remains steady but you’re also maintaining, keeping up the lifetime of all of those windmills and so on.
So really partnering at an even deeper level with the client, to get the client to maximize their value.
1. Descriptive and Diagnostic Analysis
The Transactional Model for GE was focusing on how much GE was selling, in sales of operational equipment, and in sales of parts and services. And what does GE need to do to drive up those sales. So in terms of analytics, GE needed to perform descriptive and diagnostic analytics in order to increase its sales of equipment, parts, and services.
2. Predictive Analytics
During the level of the contractual model, GE would guarantee the performance of its equipments sold to their clients. Now they need to go into the level of predictive analytics.
GE needs to predict when stuff will fail so that they can do preventive maintenance. They need to be able to predict how to operate this equipment so that it will actually stay up for 90% of the time. Finally, performance guarantees take GE to the level of predictive analytics.
3. Prescriptive Analytics.
The last model for GE is expanded customer outcomes, this is at the level of prescriptive analytics. GE’s truly telling the client(s) how they should use the equipment to maximize the value. Much more value for the client this is also much harder to do because GE needs to collect a lot of more data, analyze it a lot more carefully.
This has changed the business model because GE is actually selling not just the equipments, parts and services, GE is selling the analysis that means GE is going to be charging money for this partnership where GE can tell its clients how best to operate the equipment. So this is actually generating more value for both GE and for the clients.
By using Big data and analytics to identify emerging trends, organizations will be able to create new service offerings for their consumers. And new business models who execute them.
Using data and analytics isn’t limited to only high tech industries. Whatever sector you operate in, be it finance, healthcare, education, Insurance, Transportation, Sports, Energy, Media, Manufacturing, retail, or just about anything else, big data and analytics can and will play a critical role. Therefore, organizations using big data solutions need to keep up with its evolving nature while those still reluctant to invest should rethink their organizational policies.
If you learned something new or enjoyed reading this article, please clap it up 👏 and share it so that others will see it. Feel free to leave a comment too.