Watson Analytics

Need for Watson Analytics
If an organization is good at analyzing data and extracting relevant insights from it then decision makers can make more informed and thus more optimal decisions. But the decision makers are forced to make decisions with incomplete information. The reason?  Decisions makers/ Citizen Analysts, for the most part, tend to be mainly consumers of analytics and they rely on more skilled resources (Like Data Engineer, Data Scientist, Application developer) in the organization to provide the data driven answers to their questions. Moreover the answer to one question is just the start of another. Think of a detective interrogating a suspect. The consumer/builder model is hardly conducive to the iterative nature of data analysis. Therefore, the time it takes for these answers to be delivered to the decision makers is far from optimal – and many questions go unanswered every day.

watsonlogoWatson Analytics
So a logical solution is to provide an easier to use analytics offerings. Watson Analytics provides that value add so that more people will be able to leverage data to drive better decision making using analytics.

When we think of Watson, we think about Cognitive. And when we think about Analytics, we think  about traditional analytics (querying, dashboarding), along with some more advanced analytic capabilities (data mining, and social media analytics). So Watson Analytics is a Cloud based offering which can make analytics a child’s play even for a non-skilled user.

Watson Analytics helps users understand their data in a guided way using a natural language interface to ask a series of business questions. Example, a user can ask “What is the trend of revenue over years?” and get a visualization in response. So, Instead of having to first choose a visualization and working backwards to try answer the business question, Watson Analytics allows you to describe your intent in natural language, and it chooses the best visualization for you. Even better, Watson Analytics gives you some initial set of questions which you can keep refining.

Watson Analytics for Social Media
Watson Analytics can work on Social Media data to take the pulse of an audience by spotting trends and identifying new insights and relationships across multiple social channels allowing greater visibility into a given topic or market. It combines structured and unstructured self-service analysis to enrich your social media analytics experience for exceptionally insightful discoveries. All on the cloud!

Summary of Steps:
Watson Analytics does the following to provide insights hidden in your Big data. Mouse-over the below images to get the details of the steps.

  • Import data from a robust set of data source (on Cloud and on premise) options, with the option to prepare and cleanse via IBM Bluemix Data Connect.
  • Answering What: Identifying issues, early problem detection, finding anomalies or exceptions, challenging conventional wisdom or the status quo.
  • Understanding or explaining outcomes, Why something happened.
  • Dashboarding to share results

The 4 Personas for Data Analytics

Due to new modernization strategies, data analytics is architected from  top down or through the lens of the consumers of the data. In this blog, I will describe the four roles that are integral to the data lifecycle. These are the personas who interact with data while uncovering and deploying insights as they explore this organizational data.

Citizen analysts/knowledge workers

A knowledge worker is primarily a subject-matter expert (SME) in a specific area of business—for example, a business analyst focused on risk or fraud, a marketing analyst aiming to build out new offers or someone who works to drive efficiencies into the supply chain. These users do not know where or how data is stored, or how to build an ETL flow or a machine learning algorithm. They simply want to access information on demand, driving analysis from their base of expertise, and create visualizations. They are the users of offerings like the Watson Analytics.

Data scientists

Data scientists can do a more sophisticated analysis, find a root cause to a problem, and develop a solution based on an insight that he discovers. They can use SPSS, SAS, etc or open source tools with built-in data shaping and point-and-click machine learning to manipulate large amount of data.

Data engineers

They focus enable data integrations, connections (plumbing) and data quality. They do the underlying enablement that a data scientist and citizen analyst depend on. They typically depend on solutions like DataWorks Forge to access multiple data source and to transform them within a fully managed service.

Application developers

Application developers are responsible for making analytics algorithms actionable within a business process, generally supported by a production system. Beginning with the analytics algorithms built by citizen analysts or data scientists, they work with the final data model representation created by data engineers, building an application that ties into the overall business process. They use something like Bluemix development platform and APIs for the individual data and analytics services.

Putting it all together

Image a scenario where a Citizen analyst notices (from a dashboard) that retail sales are down for the quarter. She pulls up Watson Analytics and uses it to discover that the underlying problem is specific to a category of goods and services in store in a specific region. But she needs more help to find the exact cause and a remedy.

She engages her data scientists and engineer. They discuss the need to pull in more data than just the transactional data the business analyst already has access to, specifically weather, social, and IoT data from the stores. The data engineer helps create the necessary access – the data scientists can then form and test various hypothesis using different analytic models.

Once the data scientist determines the root cause, he then shares the model with the developer who can then leverage it to improve the company’s mobile apps and websites to be more responsive in real-time to address the issue. The citizen analyst also shares the insight with the marketing department so they can take corrective action.

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IBM Bluemix Data Connect

I have been tracking the development on IBM Bluemix Data Connect quite closely. One of the reason is that I was a key developer in the one of the first few services that it launched almost two years back under the name of DataWorks. Two weeks back I attended a session on Data Connect by the architect and saw a demo. I am impressed at the way it has evolved since then. Therefore I am planning to re-visit DataWorks again, now as IBM Bluemix Data Connect. In this blog I will reconcile the role that IBM Bluemix Data Connect play in the era of cloud computing, big data and the Internet of Things.

Research from Forrester found that 68 percent of simple BI requests take weeks, months or longer for IT to fulfill due to lack of technical resources. So this entails that the enterprises must find ways to transform line of business professionals into skilled data workers, taking some of the burden off of IT. It means business users should be empowered work with data from many sources—both on premises and in the cloud—without requiring the deep technical expertise of a database administrator or data scientist.

This is where cloud services like IBM Bluemix Data Connect comes into picture. It enables both technical and non-technical business users to derive useful insights from data, with point and click access—whether it’s a few Excel sheets stored locally, or a massive database hosted in the cloud.

Data Connect is a fully managed data preparation and movement service that enables users to put data to work through a simple yet powerful cloud-based interface. The design team has taken lot of pain to design the solution in most simplistic way, so that a basic user can quickly get started with it. Data Connect empowers the business analyst to discover, cleanse, standardize, transform and move data in support of application development and analytics use cases.

Through its integration with cloud data services like IBM Watson Analytics, Data Connect is a seamless tool for preparing and moving data from on premises and off premises to an analytics cloud ecosystem where it can be quickly analyzed and visualized. Furthermore, Data Connect is backed by continuous delivery, which adds robust new features and functionality on a regular basis. Its processing engine is built on Apache Spark, the leading open source analytics project, with a large and continuously growing development community. The result is a best-of-breed solution that can keep up with the rapid pace of innovation in big data and cloud computing.

So here are highlights of IBM Bluemix Data Connect:

  • Allow technical and non-technical users to draw value from data quickly and easily.
  • Ensure data quality with simple data preparation and movement services in the cloud.
  • Integrate with leading cloud data services to create a seamless data management platform.
  • Continuous inflow of new and robust features
  • Best-of-breed ETL solution available on Bluemix  – IBMs Next-Generation Cloud App Development Platform