Introduction to the Thought Leadership Program

In this section, the speaker introduces the Thought Leadership Program and explains its purpose.

Purpose of the Thought Leadership Program

• The program aims to drive the relevance of business analysis in today's business environment.

• It focuses on digital and new business and technology trends.

• The program was created two years ago when IIA looked at their business analysis elements in this context.

• The goal was to quickly create a lot of operating models to get content out.

Partnerships for Content Creation

• Large corporates were partnered with because they are closer to clients and have insights that can be used for larger knowledge space.

• A lot of content was authored, and partnerships with thought leaders were formed.

Business Analysis in the Age of Data

The speaker introduces a six-paper series on business analysis in the age of data, with three papers already available on the IBS website and two under review. The session will focus on two of those papers.

Introduction to Oil and Gas Industry

• The speaker shares his background in the oil and gas industry, having worked with crude oil for 10 years before moving to IT business in 2004.

• He discusses how crude oil was discovered and its significance as a major milestone in human history.

Importance of Data for Businesses

• The speaker emphasizes the influence of data on businesses today.

• He introduces two papers from the series that discuss text analytics and data analytics, respectively, and their impact on businesses.

• A panel consisting of Shesha from Accenture, Manu, Vikrant, and Rao is invited to discuss these topics further.

Text Analytics for Businesses

• Shesha explains how text analytics can help businesses extract insights from unstructured data sources such as social media posts or customer feedback forms.

• She gives an example of how text analytics helped a telecom company identify reasons for customer churn by analyzing call center transcripts.

• The panel discusses various use cases for text analytics such as sentiment analysis, topic modeling, and entity recognition.

Data Analytics for Businesses

• Vikrant talks about how data analytics can help businesses make informed decisions based on historical trends and patterns.

• He gives an example of how data analytics helped a retail company optimize its inventory management by predicting demand using sales data.

• The panel discusses various use cases for data analytics such as predictive modeling, clustering, and outlier detection.

Challenges in Implementing Analytics

• Rao talks about the challenges businesses face when implementing analytics solutions such as lack of skilled resources and data quality issues.

• He emphasizes the need for a strong data governance framework to ensure data accuracy and consistency.

• The panel discusses how businesses can overcome these challenges by investing in training programs, partnering with external vendors, and adopting best practices.

Conclusion

• The speaker summarizes the key takeaways from the session, highlighting the importance of text analytics and data analytics for businesses today.

• He thanks the panelists and concludes the session.

The Role of Oil Business and Data Insights

In this section, the speaker talks about how the oil business has been ruling the world and how even by 2050, only one-third of energy will come from renewables. The speaker also discusses data insights and how they are becoming increasingly important.

Oil Business Dominance

• The oil business has been ruling the world.

• Even by 2050, only one-third of energy will come from renewables.

• The essence of crude oil business in the past was to make human life more comfortable.

Data Insights Importance

• Data is the new oil.

• Insights that come from data are becoming increasingly important.

• 2.5 quintillion bytes of data are created each day, with 90% generated in the last two years.

• Business analysts and data architects should work towards making end customer lives more comfortable.

Using Data for Price Comparison

In this section, the speaker talks about using data for price comparison and how Amazon has become a popular platform for shopping.

Price Comparison

• People use Google or Amazon to compare prices before buying a product.

• Amazon has become a popular platform for shopping.

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Enabling Companies to Listen to Customer Needs

In this section, the speaker discusses how companies can listen to customer needs and use data insights to retain customers.

Listening to Customer Needs

• The speaker emphasizes the importance of listening to customer needs in order to enable companies like Shell, YouTube, Amazon, and Google.

• Retaining customers is crucial for businesses. The goal is to improve customer experience and increase customer loyalty.

• Data insights are key in understanding customer needs and building solutions that meet those needs.

Business Examples

• The speaker focuses on business examples rather than technology. Accenture has published papers on healthcare, high tech industry, financial services, energy, and travel tourism.

• Ram Manu and Vikrant will discuss insights from healthcare and high tech industry as well as statistical models used in these industries.

Ram Subramaniam's Journey in Business Analysis

In this section, Ram Subramaniam shares his journey in business analysis and how it has evolved over time.

Evolution of Business Analysis

• Ram started his career as a business analyst working on requirement documents such as BRDs and functional specifications.

• Over time, the field of business analysis has evolved with new methodologies such as user stories, epics, scaled agile.

• Healthcare remains a challenge due to siloed data even in advanced economies like the US.

The Value of ERP and Insights in Healthcare

In this section, the speaker discusses the value of Enterprise Resource Planning (ERP) systems and how they have evolved over time. They also talk about the importance of insights in healthcare.

Evolution of ERPs

• Companies were implementing ERPs, but people questioned their ROI.

• Dr. John Donovan compared ERPs to electricity - once set up, it can be used for multiple reasons.

• It took 20 years for manufacturing industry to perfect ERP deals.

• UI has improved significantly over time.

Importance of Insights in Healthcare

• Patients want simple, coordinated, seamless care that is personalized, transparent and secure.

• Medical records have been computerized with adoption rates increasing from 15% to 75% in 2018.

• There is now enough data available for deriving insights with some clients having up to 15 petabytes of patient data.

• Data is not directly usable; it needs to be massaged and worked on before deriving insights.

• Focus should be on getting bank for the buck by deriving valuable insights.

Conclusion

The speaker emphasizes the importance of ERP systems and insights in healthcare. While medical records have been computerized, there is still a need for better interaction between doctors and hospitals.

Patient 360

The speaker discusses the concept of patient 360, which involves providing a comprehensive view of a patient's health by considering various factors.

Personalized Medicine

• Companies are developing personalized drugs that cost up to half a million dollars.

• These drugs involve taking liquid from the patient, adding vectors to it in a lab, and re-injecting it into the patient for guaranteed success.

• Physicians are concerned about false positives generated by devices like Apple watches that monitor patients' health variables.

Socio-Economic Determinants

• Socio-economic determinants such as education level and access to healthcare can impact a patient's ability to follow treatment protocols effectively.

• Factors like travel time to hospitals can also affect the quality of care received during critical periods like the golden hour after an emergency.

Data Exchange

• Organizations must be able to exchange data seamlessly for effective patient care. This requires collaboration across multiple organizations and systems.

The Role of Data in Healthcare

In this section, the speaker discusses the different types of data available in healthcare and how they can be used to derive insights.

Types of Data

• Structured data includes medical records, claims records, membership records, and third-party sources of data. Analysis on this data can provide insights into a patient's medical condition.

• Unstructured and semi-structured data includes doctors' notes, which can be difficult to interpret. NLP techniques can be applied to these notes to gain meaningful insights.

• Streaming data from bedside monitors allows for remote monitoring of a patient's vital signs. However, the large volume of streaming data cannot be ingested into a medical record without crashing it.

• Dark data refers to multiple sources of data that need to be integrated and analyzed together.

The Future of AI in Healthcare

In this section, the speaker discusses how AI will impact healthcare and whether it will replace doctors or business analysts.

Impact of AI

• AI is expected to support doctors or caregivers in providing effective care to patients by providing them with relevant information.

• Human decision-making is still necessary when it comes to interpreting the results provided by AI systems.

Changes in Business Analyst Role

In this section, the speaker talks about how business analysts have been dealing with healthcare-related data for quite some time now.

Changes in Business Analyst Role

• Support systems have been around for quite some time now. However, new techniques are being developed for analyzing healthcare-related data.

Reimagining Healthcare Processes with Blockchain

In this section, the speaker discusses the importance of reengineering healthcare processes and how blockchain technology can be used to simplify and improve these processes.

Reengineering Healthcare Processes

• A broken process cannot be made more efficient by automation alone. It needs to be reengineered.

• Organizations need to reimagine the way they work together to deliver quality care to patients.

• Logical operating models are created for every industry, including payers and providers, which deep dive into business processes at a sub-level.

• Business analysts must understand key performance areas (KPAs), automation and AI opportunities, and data science opportunities in order to deliver effective solutions.

The Role of Blockchain in Simplifying Healthcare Processes

• Blockchain technology can be used across organizational boundaries where trust is not present.

• Business analysts are becoming like product owners and domain luminaries who use their knowledge of healthcare processes to provide effective solutions.

• Using data science collaboratively with business analysts is essential for delivering solutions that address business outcomes.

Working as a Business Analyst in High Tech Industry

In this section, the speakers discuss working as a business analyst in the high tech industry.

Working as a Business Analyst

• The role of a business analyst involves understanding what customers are asking for, their lingo, challenges they face, and providing effective solutions that address their needs.

• Precision is required when working collaboratively with data science teams to come up with the right solution for customers that addresses business outcomes.

Audience Interaction

• The speakers ask the audience about their experience working as hands-on BAs or onshore/offshore models.

Introduction to Data Narrative

In this section, the speaker introduces the topic of data narrative and discusses the challenges posed by the increasing amount of data in high tech industries.

The Challenge of Data in High Tech Industries

• The speaker discusses the implications of data for high tech industries and emphasizes the importance of understanding both the industry and the data.

• The speaker asks how many attendees have worked with high tech industry clients and explains that this industry includes many sub-industries such as consumer technologies, enterprise technologies, aerospace and defense industries, semiconductor industries, and medical tech industry.

• The speaker talks about the amount of data available today, including 16 zeta bytes in 2014-2015. They explain that this amount is expected to increase to 200 billion devices by 2020.

• The speaker emphasizes that handling this explosion of data requires a mature, disciplined, and responsible approach. They discuss how manual approaches to data governance have led to new technologies like AI and blockchain.

• The speaker explains that expectations for data are changing from analyzing past performance to predicting future outcomes. They emphasize that actionable recommendations are key.

Assetizing Data in High Tech Industries

• The speaker discusses how companies are using data as a key lever for mergers and acquisitions. They explain that assetizing data can lead to innovation in business models and products.

Rapid Speed to Market

The speaker discusses the importance of rapid speed to market and how it is not just about improving processes but also about bringing products to the market quickly.

Importance of Digital Thread Technology

• Digital thread technology is a new, data-intensive technology that helps R&D engineers turn out products at a fast rate.

• It allows companies to move away from waterfall mode of development and get into an agile A/B testing mode for rapid speed to market.

Challenges Faced by Enterprise Equipment Client

• An enterprise equipment client faced challenges with the efficiency of their sales force due to lethargic systems.

• Sales productivity was going down by 60% because it took 10-12 days to create a code and convert it into a meaningful order for clients, whereas industry standards were 3-4 days.

• The organization had more than 48 touchpoints for a sales rep before they could convert their sale, leading to $700-$800 million loss of opportunity on a yearly basis.

Improving Customer Experience

• Companies are looking for ways to improve customer experience as well as employee experience and partner experience holistically.

• One example is working with an A&D client who faced challenges with their service engineering partners due to aging workforce and difficulty tracking aircraft location.

End-to-end Beta Experience

The speaker discusses how companies can improve end-to-end beta experience for customers, employees, and partners.

Using AI in Sales Process

• AI can be used in the sales process to make codes more meaningful and put prices with the right discounts in the first shot, increasing probability of sales.

Challenges Faced by Service Engineering Partners

• Service engineering partners face challenges with an aging workforce and difficulty tracking aircraft location.

Importance of Optimized Customer Experience

• Companies are looking for ways to improve customer experience as well as employee experience and partner experience holistically.

Remote Access and Customer Experience

In this section, the speaker discusses how companies are using remote access technologies to enhance customer experience. They can rely on third-party vendors who use holograms to provide real-time service without engineers having to travel to remote locations.

Remote Access Technologies

• Companies are connecting their systems to R&D centers through remote access technologies.

• Microsoft HoloLens is used for remote access and Skype calling features.

• Blockchain technology is used for data-intensive tasks such as provenance of supplier paths.

Enhancing Customer Experience

• Remote access technologies allow companies to provide real-time service without engineers having to travel.

• Data is heavily relied upon for innovation and product development.

• The high-tech industry is seeing a shift from traditional hardware products towards components or software-based services and platforms.

• Traditional product manufacturers are facing a value crunch as the value migrates away from hardware products towards components or platforms.

Value Migration in High-Tech Industry

In this section, the speaker talks about the shift in value within the high-tech industry. Traditional hardware manufacturers are facing a value crunch as the value migrates towards components or platforms.

Shift in Value

• The high-tech industry has seen a shift in value from traditional hardware products towards components or software-based services and platforms.

• Traditional product manufacturers face a value crunch as they lose market share to component makers or platform players.

• Samsung, traditionally a manufacturer of hardware products, has moved into component manufacturing as that is where the value lies.

• The shift in value is enabling new business models to emerge, such as platform ecosystems.

Conclusion

In this section, the speaker concludes by summarizing the key points discussed in the previous sections.

Key Points

• Data is heavily relied upon for innovation and product development.

• The high-tech industry has seen a shift in value from traditional hardware products towards components or software-based services and platforms.

• Traditional product manufacturers face a value crunch as they lose market share to component makers or platform players.

• The shift in value is enabling new business models to emerge, such as platform ecosystems.

Moving to an As-a-Service Model

In this section, the speaker discusses how companies are moving towards an as-a-service model, which is less of a capital expenditure (CAPEX) and more of an operational expenditure (OPEX) model. The speaker gives examples of Rolls Royce and Michelin, who have implemented this model successfully.

Rolls Royce's Engine-as-a-Service Model

• Rolls Royce has started giving their engines on an as-a-service basis to airlines.

• Airlines use the engines for the amount of time they need and pay based on fuel usage and payload.

• Engines are 24/7 connected through digital technologies to Rolls Royce headquarters for real-time maintenance forecasts.

Outcome-Based Models

• Companies like Michelin are moving towards outcome-based models where they offer tires as a service to trucks.

• If trucks run on Fee Fuel, Michelin promises a 7% savings in fuel efficiency.

• Outcome-based models require changes across the entire ecosystem from marketing to sales, logistics, distribution, pricing, and after-sales support.

Importance of Data in As-a-Service Models

• As-a-service models generate vast amounts of data that need to be analyzed intelligently.

• For example, Rolls Royce's engine has over 25,000 sensors embedded into it that pull data every minute.

• Salespeople must get insights from this data to understand customer problems and renew contracts regularly.

Overall, companies are moving towards as-a-service models because they offer greater flexibility and cost-effectiveness. However, implementing these models requires significant changes across the entire organization. Additionally, analyzing the vast amounts of data generated by these models is crucial for success.

Techniques for Business Technology Analysts

In this section, the speaker discusses techniques relevant to business technology analysts and the capabilities they need to build.

Transforming into a BTA

• As a business analyst, it is important to transform into a role of Business Technology Analyst (BTA).

• The BTA role involves transforming oneself into three different areas: industry BAs, data science.

Challenges in Data Science Projects

• According to Gartner, 60% of data science projects fail.

• Understanding the business is paramount as data scientists are very co-technical and need clear problem statements.

• Lack of management support can lead to issues with accessing necessary data for models.

• Poor collaboration can be an issue when dealing with IoT devices pulling data from various functional teams.

Problem Refinement Techniques in Design Thinking

In this section, the speaker discusses problem refinement techniques used in design thinking. They introduce the "problem tree" technique and explain how cross-functional teams focus on their specific problem sets. The speaker also emphasizes the importance of aligning technical teams with business goals.

Problem Tree Technique

• The "problem tree" technique is used to refine business problems.

• Cross-functional teams focus on their specific problem sets.

• Technical and business teams must be aligned with business goals.

Challenges Faced in Data Science Projects

This section covers some of the challenges faced in data science projects. The speaker explains how poor adaptability can be a pain point when implementing solutions.

Poor Adaptability as a Pain Point

• Implementing solutions with poor adaptability can be a pain point.

• Business may not understand data models, which can lead to poor adoption of new systems.

Lifecycle of a Typical Data Science Project

In this section, the speaker discusses the lifecycle of a typical data science project and categorizes it into different stages. They also list down different activities and techniques that are involved in each stage.

Use Case: Data Explosion IoT

• Outlier detection is one technique that can help address problems caused by multiple sources and formats of data.

• Box plots are an example of visualization techniques used for outlier detection.

Importance of Proper Data for Machine Learning

The speaker discusses the importance of proper data in machine learning and how outliers and missing values can skew results.

Outliers Can Affect Averages

• Outliers in data can significantly affect the average, leading to skewed results.

Importance of Proper Data Cleaning

• 60% of time spent on machine learning is dedicated to ensuring that data is properly cleaned, accessible, and formatted.

• Missing value analysis is a useful technique for identifying and addressing missing values in data.

The Need for Data Science Tools

• Excel sheets are not sufficient for analyzing large amounts of data.

• Python and other data science tools are necessary for processing large datasets.

Controlled Explosion of Data?

The speaker questions whether the increase in available data is truly an explosion or if it's a controlled phenomenon.

Examples from Healthcare Domain

• In some healthcare systems, doctors rely heavily on computer analysis rather than personal interaction with patients. This can lead to longer wait times and unnecessary tests.

• In some cases, traditional remedies may be more effective than relying solely on data analysis.

The Role of AI in Healthcare

In this section, the speaker discusses the importance of human touch in healthcare and how AI can be used to augment doctors' abilities.

Importance of Human Touch in Healthcare

• The speaker emphasizes that while data plays an important role in healthcare, at times human touch is involved.

• The speaker gives an example of a doctor who hardly spends five minutes with a patient and doesn't know about computers.

• The speaker explains that doctors are spending more time looking at a computer instead of looking at the patient, which is not ideal.

Use of AI to Augment Doctors' Abilities

• The speaker talks about using AI as a tool to make it easier for doctors to enter data into electronic medical record systems (EMR).

• In countries like the US and UK where there is a lot of litigation, doctors have to write detailed notes which can be difficult to read. The speaker explains that AI can help simplify this process.

• The goal is to make AI an integral part of doctors' workflows so that they don't have to work for the computer but rather the computer works for them.

• Accenture has a service called clinical optimization and performance improvement which helps hospitals reimagine their business processes by simplifying collaboration and exchange of data in a seamless way using AI.

• Companies are trying to make it easier and simpler for doctors to work with AI without replacing them.

Overall, this section highlights how AI can be used as a tool to augment doctors' abilities rather than replace them.

The Relevance of US Healthcare Spending to Indian Healthcare

In this section, the speaker discusses the relevance of US healthcare spending to Indian healthcare and how data science can help with automation in India.

Automation Challenges in India

• Extracting data from handwritten IRDA prescribed format for providers is a challenge for automation in India.

• Converting doctors' handwriting into intelligence has been a difficult exercise with accuracy ranging between 10% to 40%.

Electronic Medical Records

• Implementing electronic medical records will provide an integrated view of the patient and enable care at the right time and in the right way.

• The challenge of implementing electronic medical records is much larger in India due to multiple factors.

Overcoming Data Science Failures

In this section, the speaker discusses how to overcome data science failures.

Importance of Innovation

• To be innovative, we need to look at data from multiple places such as structured systems, unstructured systems, doctors' notes, bedside monitors, and variables.

Creating Multiple Models

• To draw insights from data, we need to create multiple models and figure out which model works best.

The Importance of Business Knowledge in Data Science

In this section, the speaker emphasizes the importance of business knowledge in data science and how it can help to determine whether a particular dataset is suitable for analysis or not.

Understanding the Right Data Set

• Even with the right data set, it's possible to come to a conclusion that the data is not working for analysis.

• It's important not to get carried away by models and instead focus on whether the outcome is coming as expected.

• Regularly checking accuracy and changing model parameters can make it relevant and applicable for business needs.

Working with Data Scientists

• A business analyst (BA) must work closely with a data scientist (DS).

• Trial and error are necessary when trying out different models.

• Certain models may work initially but fail over time. It's important to have the ability to ditch a model and take another one.