Building an analytics organization

Analytics Roadmap

Analytics has been a key area of discussion in corporate meetings for many years now. Leaders across industries recognize that, in today’s competitive landscape, insight-driven decisions are the key to developing a competitive advantage. However, the use of analytics to create a competitive advantage is about to become even more widespread. The increased growth in data sources and the ubiquitous nature of analytics is creating an environment where robust analytics capabilities are needed just to keep pace. To prepare for this environment and try to attain the most value from their data, many companies are evolving beyond just using analytics and are transforming themselves to build an insight-powered enterprise.

Many companies are accelerating their investments in analytics capabilities and strategies. Companies are establishing a center of excellence for analytics led by a Chief Data and Analytics Officer (CDAO). Companies are also creating agile analytics governance and value realization offices to help ensure that they are getting an appropriate return on analytics investments. In addition, they are paying close attention to talent sourcing and planning as talent is in short supply.

They are focusing on faster deployment of new analytics capabilities and tools by piloting with the intent to scale. Finally, they are focused on raising the analytics IQ of the entire organization through training programs and new ways of working, such as using immersive analytics environments.

This focus on building analytics capabilities can increase as companies come under pressure to demonstrate the value from analytics. High performing companies are embedding predictive analytics insights into key business processes twice as much as low performers. They are winning the competition for analytics talent through multi-pronged talent sourcing strategies, they are leveraging more advanced tools and techniques, and investing at a much higher rate. In fact, a lot of the high performers plan to significantly increase their analytics investment over the next three years, compared to a very small percentage for the low performers. This is creating a competitive landscape of “haves” and “have nots” in which many companies are undertaking major transformational initiatives to catch up with the high performers who have been building their capabilities for years.

Here are 5 steps to bring analytics into an organization:

Analytics Organization Roadmap
5-Steps to an Analytics Organization





Step 1: Establish a Center of Excellence

Companies with high maturity are more likely to have established a Center of Excellence (CoE) for analytics activities with a concentration of talent and resources.

Organizations need to focus their strategy and planning activities, around activities such as analytics modeling and structured reporting, as well as strategic functions like supplier management, talent planning, portfolio governance, and outcome tracking. Establishing a center of excellence for key resources builds capabilities, but it also supports consistency and high standards while allowing functional resources to focus on key business problems and applying insights.

While this trend continues, companies also realize that it is important to counterbalance this collection of specialized resources with local decision-making closely connected to the business functions. Some companies distribute certain activities back out to the business functions once the standardized processes are in place. This is similar to the path a large bank, an early adopter of analytics, has taken over the course of its journey. The company needed to put people with data analysis skills close to the business operations to solve key problems. This was after they had spent years creating a center of excellence focused on building foundational analytics capabilities.

Analytics CoE

Chief Data and Analytics Officers

Leadership is another key element. A lot of companies are elevating analytics by creating a Chief Data and Analytics Officer (CDAO) role responsible for both the vision and the implementation of the enterprise analytics strategies.

The CDAO is often responsible for developing goals, strategies and plans to support the information, reporting and analytical needs of the company, but also acts as an agent to change the analytics culture of the company. One of the CDAO’s biggest challenges is how to design an effective operating model that will convey this change throughout the organization. CDAOs of large, high performing organizations find that the analytics journey used to be an out-of- body experience that most struggled to understand, while today analytics is an in-body experience that needs to become part of the cultural DNA.

Step 2: Create Agile governance

Traditional governance models are often thought of as sluggish, with a focus on standards and processes, but today, high performing analytics leaders are building thin, horizontal governance structures, focused on outcomes and speed to value, rather than creating hierarchies that are slow to adapt.

These structures take a “test and learn” approach to rolling out new capabilities. They establish success criteria, with regular checkpoints for measuring performance against these criteria. They also employ “fail fast” techniques, rapidly rolling out new ideas and capabilities and testing them repeatedly. If an idea is not working, it is dropped quickly, so that the company does not continue to invest in something that does not add value.

Analytics Agile Governance
Structured Innovation

Another element of agile governance is a structured innovation process. Leaders may set up innovation or “SWAT teams” with a mandate to focus on key business questions for a concentrated period, often as short as 30 days. Or, they may use a form of open sourcing; they could establish an Innovation Forum through which everyone in the organization (including contractors) could submit ideas for solutions to problems posed by senior leadership. The leadership team can choose the best idea at the end of each designated period and provide resources for the winner to go forward and build out the analytics solution.

Executive Scorecards

Perhaps the most critical part of establishing an agile governance structure is ensuring that it uses the key metrics that are most important to the business. To this end, CDAOs are designing executive scorecards to track performance against these key metrics, which might include:

  1. Speed to standing up priority capabilities—How quickly was an initiative tested and/or piloted
  2. Pace of adopting new capabilities— Identifying and tracking the adoption of priority capabilities, and compressing the time from pilot to industrialization
  3. Value realization—Defining overall value delivered through the analytics initiative or project
Value Creation Offices

Leaders are also instituting value creation “offices” to spearhead outcome tracking against key metrics and to ensure that the value from analytics is realized. These “offices” have involvement from senior leaders with accountability for analytics as well as program management office resources who can develop templates and processes as needed. Some of the key success factors for these offices include their ability to design and implement a closed loop process for identifying and measuring the value of each analytics project or initiative. If the analytics initiative fails or does not meet the identified baseline value, the closed loop process should incorporate that learning back into future cycles.

Step 3: Create an inter-disciplinary, high-performing analytics team

Companies with advanced analytics capabilities field teams with diverse skills. Talent is organized effectively and there are innovative programs to keep the best talent engaged. These companies use multiple talent sourcing options, including, but not limited to, internal development and hiring, but they are also attracting more talent from other companies.

High-performing Analytics Team
Analytics Team
Analytics Pod Structures

It remains difficult, if not impossible, to find all the skills required for analytics success within any one individual. Many mature companies, therefore, are establishing “pod” teams which have a mix of roles, including data scientist, analytics modeler, visualization expert, data engineer, business analyst and business domain expert. By combining these capabilities, analytics pods can take an integrated view of business problems.

More mature companies have a greater percentage of modeling and discovery resources aligned with a standalone analytics function. However, skill requirements change as analytics capabilities mature; companies in the middle stage of analytics maturity call for more data modeling and stewardship, while mature companies—who are past the core challenges—need more data scientists and other specialized roles.

Analytics Career Paths

Analytics talent tends to follow non- traditional career paths in response to the value that this talent tends to place on becoming subject matter experts in finding insights in large data sets, often instead of following traditional managerial career paths. A lot of companies are encountering similar talent planning challenges and are responding with a variety of methods including varied work assignments and compensation based on technical competency.

Talent Sourcing

Leading analytics companies create mechanisms to source the best talent for their organization. They may, for example, build their brands internally and externally, marketing themselves as the destination for top talent. Companies use crowdsourcing, sponsoring competitions and offering prizes to solve problems. Still others run open challenges or “hackathons” that provide incentives for people to respond to and solve business problems.

Some companies even partner with academia to source talent. They can partner with   leading universities to open data analytics centers. These labs can focus on data analytics, developing data research and innovation to solve problems ranging from assortment optimization, social media and market trends to large- scale data initiatives.

Talent Retention

Analytics talent is in short supply. Given that there does not appear to be a solution to the talent shortage in the near future, it is important for analytics organizations to focus on talent retention by developing reward and incentive programs that keep these individuals engaged. We have seen that a key to retention is keeping the analytics resources challenged. Many of the financial services companies are seeing a boomerang effect as analytics talent left to go to high tech companies in the last 5-7 years and that talent is now returning to Financial Services as the business problem set to solve is more interesting and complex than at the high-tech companies, which keeps the advanced analytics talent engaged and constantly learning. This engagement has resulted in higher retention over time.

Step 4: Deploy new capabilities faster

One of the biggest challenges for analytics organizations is to establish an operating model with a view to scaling priority capabilities, especially considering the roadblocks posed by existing analytics skills and in-place data architectures. Leading analytics organizations deploy new, agile technologies, as well as hybrid architectures and specifically designed toolsets, to help achieve speed to capability and desired outcomes. One distinguishing characteristic of fast-moving companies is that they pilot with the intent to scale; that is, they establish the right mindset, processes and accountability in advance, then move quickly to test, learn, refine and implement.

Scaling priority capabilities requires new approaches and mindsets for many organizations. These organizations may need to “unlearn” what has already been learned; for example, they may need to bring the data to the analytics, rather than the other way around. There may also be differences in the way teams with statistical backgrounds tackle scaling problems, using a hypothesis/test/verify framework.

Many organizations have already begun addressing these issues. By prioritizing the scaling of capabilities, companies can optimize analytics investments, rationalize vendors and suppliers, improve talent acquisition, and rationalize data and tools.

Analytics Capabilities
Deploy Analytics Capabilities


The consumer banking operation of a major multinational bank was suffering drop-offs in their net promoter scores and wanted to understand why. They had a very complex and convoluted technology environment with a slow, costly and inflexible legacy infrastructure. It took on average 9-12 months to develop and deploy new models (for example, credit risk scoring for loans or credit line increases). The bank had a vision of leveraging analytics to improve their overall customer experience yet they needed the technical capability to enable this strategy. They also wanted to leverage big data technologies as an alternative to traditional approaches for both speed to capability and cost benefits.

They did a data and analytics strategy evaluation leading to a roadmap for them to become more dynamic and more real-time, and to improve the customer experience. The first area of focus was to revamp the technology infrastructure so that the bank could achieve desired outcomes. The next step was to develop a data library using open source technology rules engines for the real-time scoring of credit line increases for customers. This helped produce an increase in annual operating income.

With the data and rules engine, the bank could identify the cause of the drop-off in customer satisfaction—and it was not what they had thought. The most affluent customer segments were the most digitally savvy, and they were dissatisfied with their experience on the Web and on mobile devices. The bank had thought that service fees were the issue, and while service fees were an issue across all segments, the key issue for the most profitable customers was the online and mobile experience.

The bank is now moving towards an “always on” capability to improve the customer experience and thereby improve customer retention and profitability. By leveraging an inter-disciplinary blend of skills— data scientists, visualization experts, data architects, and business domain experts—the bank could understand the insights and act quicker to design and implement solutions. This innovative project at the core of the bank’s operating system helped the bank realize a faster return on its technology and analytics investments.

Step 5: Raise the company’s analytics IQ

The final distinguishing characteristic of leading analytics organizations is their commitment to raising the “analytics IQ” of all roles within the enterprise.

They may, for example, implement an Analytics Academy that provides analytics training for functional business resources in addition to the core management training programs. Within the analytics organization, the Academy may focus on developing business and communications skills to make sure that the insights obtained are put to good use within the business units. And it may provide training courses to raise the business and analytical acumen of the IT organization.

Analytics IQ
The Intelligent Enterprise

Leading analytics organizations have a vision of what might be termed the “intelligent enterprise.” They are training resources to use new tools and techniques to improve decision-making throughout the enterprise and they are also implementing innovative technologies such as advanced data visualization to communicate the value of analytics to business units, core functional teams and IT.

Some companies build their own immersive environments that leverage visualization techniques to provide greater context for the information presented as well as the trends that are being illustrated. They are moving away from traditional presentation tools and are leveraging more interactive tools to improve collaboration. To encourage use of these new interactive tools, these companies might set up contests where resources can earn badges and certifications based on insights developed and actions taking within the tool.

This holistic, interactive learning approach where business, analytics, and IT resources are equipped with new immersive tools, techniques, and formal training opportunities allows companies to:

  1. Activate leaders by focusing time, funding and attention on desired behaviors and skills;
  2. Shift behaviors and mindsets to engrain new habits, and
  3. Embed behaviors into business processes and metrics, all in service of driving a cultural transformation.

Solving the KYC conundrum with AI and Blockchain

In the financial world, KYC is a very manual and time consuming process. Artificial Intelligence (AI) and Blockchain combined together can streamline the process of KYC in financial services.

First, let’s take a look at what these terms mean:


Blockchain can best be described as a digital database, which, unlike a traditional database, is characterized by three main features: decentralization, immutability and transparency. By using cryptography, blockchain provides a decentralized database or a “digital ledger” of transactions that everyone on the network can see. This network is basically nothing but a chain of computers that will each have to approve an exchange before it can be deemed to be verified and recorded.



KYC Onboarding

Know Your Customer (KYC) is a mandatory process which needs to be executed by financial institutions. This essentially boils down to understanding the background of the entity to whom you are providing financial services regardless of whether they are individuals or businesses.

For individuals, the process could be simply getting a proof of identification and address and more detailed information like understanding their wealth sources, commercial interests and status.

For companies, it is basically getting to know the actual business, history, the entity structure, leadership and shareholders. Additionally, they will need to show how the business operates and makes money.


Anti-Money Laundering (AML) is the art of knowing patterns of potential illegal money flow at the transaction level. Banks get into trouble when they get caught moving money around especially if the money has illegal sources or is used for wrongful purposes, such as funding terrorism.

So, what is the problem with the KYC process today?

KYC is a tedious and expensive part of onboarding a new client. Each financial institution must perform their own KYC. This essentially means that financial institutions have a steep cost of acquiring a new customer. Let’s see how the KYC process works for an individual.

Some of the basic steps in the KYC process are:

  1. Get personal information (Name, address and source of wealth).
  2. Get proof(s) of personal information (Documents to prove name and address and source of wealth).
  3. Storing the personal information (Audit trail to show regulators that this has been done).
  4. Background checks, also known as Customer Due Diligence (CDD) (Additional checks on the background).
  5. Ongoing monitoring of customers (Check for address change, company change, status, illegal/criminal activities etc.).

All new customers applying to every financial institution must go through the same process.

Where is the bulk of the time spent in the KYC process?

  • Reading and making sense of the various connections and documents.
  • Potential multiple 2-way communication attempts with the customers to request additional information.
  • Gleaning through false positive alerts and focusing on real issues.
  • Being able to reason and look at documents like a detective to identify a potential fraud.
  • And, of course, repeating this every time there is a change to the customer including job, address, status etc.

KYC processes can be very expensive and inefficient while giving the customers a very poor user experience. It can take up to 10-20 days to onboard a customer via all the mandatory checks including numerous documents needed to be submitted and verified. This is extremely tedious for new customers and does not bode well for financial institutions since this process does not generate any revenue. To top this off, the fines that are imposed for wrong execution of the KYC process can be high. You need large teams to handle transactions that fail AML checks, and usually these have a false positive rate higher than 99%, resulting in huge inefficiencies.

So, how can AI and Blockchains help in making the KYC process more efficient?

AI plus Blockchain

As a start, creating a shared repository between the various banks and financial institutions on the blockchain and bringing customers onto it will help alleviate the customer identification step.  Blockchain creates a tamper proof, highly secure repository that automatically becomes proof for regulators as they too can become a node on the blockchain and view all information.

But how about the CDD (Client Due Diligence) step, the one that requires tons of manual effort to read, reason and analyze information from various sources? Blockchain is great since it comes with immutability, higher fault tolerance and security, shared ledger and automated smart contracts but it cannot play a role in the reasoning part of CDD. This is when we can bring in Artificial Intelligence (AI) technologies to augment blockchain to assist with the KYC/CDD challenge in a holistic way.

AI brings us object recognition, reasoning, anomaly detection and deep analytical capabilities. We can use the OCR capabilities to read and understand the various documents and store them on a blockchain based shared immutable repository that is visible to regulators. AI can perform deep analytics and reasoning on the information to detect anomalies and reduce false positives. Additionally, we can create distributed apps (dApps) and smart contracts to auto alert the participants on changes to a customer’s status or position.

So, although blockchain technology on its own might not solve the KYC conundrum completely, the combination of Artificial Intelligence (AI) and Blockchains could help in making the KYC process more palatable and efficient in the long run.

The convergence of AI and Blockchain in Healthcare

Blockchain combined with Artificial Intelligence (AI) has the potential to revolutionize the field of healthcare in the next few years. Let’s look at each of these concepts first.

Artificial Intelligence

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) was originally introduced as a term to emulate the human brain and try to solve problems in the world using machines but with a holistic human approach. There have been several advancements in technology to satisfy our strong desire to augment the human brain. Big data brings the capacity to store large amounts of data and AI does a great job of processing and translating the data in an intelligent manner into consumable tools. A truly artificially intelligent system is one that learns on its own, one that’s capable of processing huge amounts of data and create associations and intelligently mimic actual human behavior.

The increased use of artificial intelligence and machine learning is disrupting the areas of medical research and treatment. These advanced technologies provide researchers access to tons of data via clinical studies and journals on several genetic disorders. This could potentially shorten the amount of time it takes to develop cures by analyzing of a lot of comprehensive data very quickly. AI can be used to generate insights which can assist in the discovery and clinical development of pharmaceutical medicine.

What are some use cases for AI in Healthcare?

Do you remember when we started using robotics in manufacturing? Well, in a similar fashion, AI is making its mark in healthcare by automating a few daily, repetitive tasks. Let’s look at the example of detecting cancer. We can leverage the power of deep learning, write algorithms and develop  models that can be trained to distinguish between sets of pixels in an image that represents cancer versus sets that don’t. At this point, AI technology can go through millions of medical images (X-Rays, MRIs, CT scans, etc.) in a single day to detect patterns and anomalies that normal humans just cannot do.

Additionally, these algorithms are constantly learning and evolving and getting better at making these associations with each new data set that gets ingested into the data stores. Pathology and radiology might see tangible benefits soon as technology companies will bring these deep learning algorithms to healthcare providers. In fact, some companies are already doing this. FDA recently approved an AI-powered medical imaging platform that helps doctors analyze and diagnose anomalies in the heart. This is the first time ever that the FDA has approved a machine learning application for use in clinical environments.

Now, let’s look at the practicality of using AI as opposed to normal human beings. We know that humans get distracted easily but computers don’t get bored or distracted. That, combined with immense compute and processing power, AI is exponentially better than us at analyzing data.

Let’s look at the example of IBM’s Watson. Watson could analyze genomic data from both tumor cells and healthy cells and was ultimately able to recommend actionable insights in a mere 10 minutes. The same data would have taken a human roughly 160 hours to analyze. Apart from diagnoses, AI is also being used in the pharmaceutical industry to help with extremely time-consuming monotonous work of discovering new drugs, and a lot of companies are jumping on the bandwagon.

Gartner recently predicted that by 2025, 50 percent of the population will rely on AI-powered virtual personal health assistants for their routine primary care needs. These virtual assistants (like Siri) would be out main interaction engines for out health devices and the machine learning algorithms would be working round the clock to analyze our biometric data. These assistants would tell us about current state of health, acting as a sort of medical informer and alert us when it’s time to see a physician.

With the amount of data generated and available to mankind today combined with the various advancements in technology, healthcare is transitioning from reacting to preventing and disrupting the way care is delivered. Artificial intelligence is and will be saving our lives for years to come.

Now, let’s see what Blockchain is all about.

So, what is Blockchain?


How do you make a transaction currently? By using trusted middleman like a bank, right? The difference with blockchain is that it allows consumers and suppliers to connect directly, eliminating the need for a third party. By using cryptography to keep the exchanges secure, blockchain provides a decentralized database or a “digital ledger” of transactions that everyone on the network can see. This network is basically nothing but a chain of computers that will each have to approve an exchange before it can be deemed to be verified and recorded.

Blockchain can best be described as a digital database, which, unlike a traditional database, is characterized by three main features: decentralization, immutability and transparency.


As a major deviation from centralized structures, a distributed ledger technology like blockchain exists in the form of a network: essentially a copy of the blockchain is located on each participating computer. This makes the data integrity extremely strong and resistant to hacking. For example, in the traditional financial world, the bank is the central authority that everyone must trust, in the world of blockchain, the bank can be eliminated. All cryptocurrencies (e.g. Bitcoin) function according to this principle. All transactions take place completely autonomously through the users and their end devices.


Data can only be added to the blockchain, but it cannot be changed or deleted. All data remains in its original state. This makes every transaction visible to every user. You can change the access rights but that’s about it.


All the users involved in a blockchain can view the data at any time without restriction by having the right privileges. This way transparency is established without any additional layer of software or authentication.

Now, let’s look at some of the applications of Blockchain in the Life Sciences field

Block Chain in Healthcare

The element of transparency appears to be highly misconstrued in the healthcare sector. However, transparency in this context does not mean that the patient is semi-transparent and unauthorized persons can view his data. In fact, it’s exactly the opposite. Blockchain has the potential to bring the healthcare sector much closer towards patient centralization.

The management of medical data in the form of digital patient records based on blockchain technology can make the patients have complete control of their own data. We know that there are public blockchains, such as those used to trade cryptocurrencies. But, there are also private blockchains that only permit access to those who have permission to access them. The possibility of storing data in external databases also exists with pointers (references) in the blockchain.

You can potentially store x-rays and CT scan images and medication histories in external databases with pointers in the blockchain. So, essentially, data is available on the computer systems in place at the relevant healthcare provider. An index will point to this data in the blockchain and manages the corresponding access rights. The patient can then decide who can access his data, and can access it at any time. This simplifies the cooperation between the different specialists treating the patients, and the storage and administration of their personal data.

Additionally, sensor and medical device data can be added to the blockchain and evaluated. Imagine the potential of an individual early warning system that can be created for the patient keeping him informed of his health status. In medical research, blockchain technology is also useful for managing the results of studies. The reliability of research results can be tracked objectively because it is transparent and secure from tampering. The question of who made the initial discovery can also be addressed in this manner, since all the data and transactions are transparent and immutable.

I strongly believe that blockchain can radically improve data sharing, data security, interoperability, patient engagement, health information exchange (HIE) and R&D. For example, in population health management, providers can use blockchain to progress in clinical research patient safety event reporting, adverse event identification, public health reporting and precision medicine.

So, how Blockchain and AI can work together for a better healthcare?

Block Chain and AI in Healthcare

Technology is democratizing data and enabling real-time analytics for actionable insights. We can combine Blockchain and AI to analyze data sets that cannot be analyzed together due to regulations or data privacy concerns. Insights that were unaffordable due to a manual curation process will be made accessible by biotech companies, treatment centers and even patients.

We can use blockchain to keep the data decentralized. Imagine a world where patients don’t worry about sharing their behavioral data? Additionally, let’s say they are able to share their deeply personal medical data with pharmaceutical companies. This can be possible with blockchain since the data won’t reside with pharmaceutical companies but in immutable blocks. If they take part in a trial and the drug gets approval, patients can partake in the benefits instantly through smart contracts. Blockchain and AI could enable a structural shift where all parties share data in a decentralized fashion, wherein the system could still collectively use the data to make smart decisions. This could overthrow the legacy hurdles of healthcare i.e. data lying in different places, strong regulations restricting the sharing and analysis of that data, and weak incentives for sharing research and training data.

Future of Healthcare

The future of healthcare is very bright. We can combine technologies like AI, Cognitive computing, machine learning and blockchain to look at much larger and diverse data sets. As with any emergent technology, blockchain’s use for healthcare data is a work in progress, but they have predicted that within five years, healthcare blockchain will be the new norm for the sector.