D. Justhy's Blog

"Getting to Yes, Now!"


Why Cloud Migration Needs a Sound Data Strategy

In our previous post, we discussed how the data value chain can be understood using the Fibonacci spiral. The data supply chain is important for planning security as it allows organizations to implement a security zoning model (more on that below) based around the relative importance as well as the data sensitivity of particular data systems. And security is a particularly important concern when planning a cloud migration as organizations need to strike the right balance between securing their data and at the same time, enabling the high value digital business models, which need data to be enabling democratic use, to serve the business eco-system. Creating economic value with data in the digital age, is a non-negotiable need. This is why there is a need to adequately secure and control data in the cloud. This is why, a fundamental step is to choose a cloud host who can ensure the security and reliability of sensitive, revenue-producing data – if at all you want to migrate it.


Security zoning model

Fig 1 : Inbound – Data Supply Chain


Fig 2 : Outbound – Information Supply Chain

In the images above, data is categorized by zone, which tells you about the data’s value and sensitivity based on its relative importance to the organization. Data that increases in value also becomes increasingly sensitive to a breach, and the closer it is to the center of the spiral. This relative value is demonstrated in the image below. The relative enterprise value of any application or data system can be accurately determined using tools such as the The Data As An Asset (DAAS) Index.

Fig 3 : Value VS Sensitivity to Data Breach

Data in Zone 0 is the most valuable to the organization and most susceptible to data breaches. As this data has the most value, it should be considered carefully during cloud migration. And if you want to make the move to the cloud, you will need to consider the following:

  • What’s critical and what can be migrated and when?
  • Some of your team’s day-to-day security tasks will be taken over by your cloud provider. Clearly defining security responsibilities and ensuring that your team continues adopting a proactive stance towards security becomes necessary. A detailed process flow complemented by a Responsible Accountable Consult Inform (RASCI) are practical tools available to most organizations.
  • To be on the safe side, you could consider migrating non-critical data and applications so that issues don’t have a major impact. For instance, you can start with your data archives first and move some of the more important data when you’re happy with the performance and security of your migration.

With a solid data strategy and a well-managed data value chain, organizations can confidently identify the most valuable and sensitive data. In today’s age of unabated digital disruption, it has become imperative for organizations to understand their data deeply and create a strategy compatible with their business model. As a digital disruptor, cloud migration can be successful only if a thoughtful, effective data strategy is in place. And the big data and business analytics of the organization can be aptly supported by making the most of a scalable, cost-effective cloud infrastructure.

Enjoy your cloud!

The One Skill That Makes A Fearless Digital Age Leader & A Digital Age Professional

Leadership has many definitions in different contexts. One definition is that “leadership involves the capacity and will to rally people to a common purpose together with the character that inspires confidence and trust“. Another definition of leadership, a process of social influence in which the “leader enlists the support of others to accomplish a common task”.

In the digital age or information age, the role of the leader remains the same while the approach slightly shifts to accommodate changes in technology in terms of Artificial Intelligence, Internet of Things, networking, open source technology, and smartphones.

The One Skill A Fearless Digital Age Leader Must Possess

With the knowledge society taking over, the one skill a fearless digital age leader or professional needs to nurture is that of openness to learn and adapt to the digital revolution.

A survey was conducted whose participants included about 1,293 millennial employees based in the U.S., U.K., India, France, Brazil, and China. The results published in Millennial Compass Report indicate that the participants highly admired those with knowledge and experience rather than power or authority. Now, keep in mind that the millennials constitute a majority of the workforce.

The benefits of openness to learning

  • Understanding technology to make intelligent use of data and processes to not only provide direction to the team but to guide the integration of trends across all sectors is secondary to this openness to learning.
  • Openness to learning the technological shifts in areas such as cloud computing, robotics, and automation will enable the digital age leader to be fearless in integrating these into the functions of the business organization.
  • Constant learning will also help the leaders cultivate a symbiotic interaction and relationship with technology.
  • It is important for digital age leaders to think like journalists and adapt investigative journalism. While the digital age leader is open to learning all that technology has on offer, he or she does not force conclusions but rather offers evidence based insights to be debated on.
  • With the openness to learn comes the willingness to constantly experiment and change as necessary as change is the only constant in the new digital age. When leaders are fearless in learning and understanding the information, thinking can become expansive and forward looking.
  • With these traits, it is easy for the digital age leaders to develop teams that are integrated with the technological advancements and think dynamically as well. Such adoption of expansive thinking allows teams to shed traditional and conventional thinking in lieu of the demands of the highly competitive digital era.
  • As data becomes more extensive and specialized skills are developed, informed leaders can better integrate processes with these hyper specialists.

Technology is constantly evolving. The only way to remain a “Leader” in this digital age is to embrace it!

The Relevance of Language in the Digital Age

The World Wide Web has transformed the way we communicate; yet it has been detrimental to the learning of languages and enhancement of language capabilities at the individual level. Where before, the spoken and written language required at least some attention to grammar and semantics, today it can be likened to a software code: enough if understood and gets the job done. In the digital age, there is greater emphasis on the pragmatic aspect of language as a driver of communication.

It is not difficult to imagine why. The world has become a smaller place and lives have become busier. Time is at a premium and ironically, the acceleration in work speed courtesy of computers has only increased pressure on humans to work at breakneck speed, trying to get as much done in as little time as possible. In this scenario, language has taken a backseat and the value of quantifiable data – numbers and statistics – has increased. Simultaneously and somewhat unfortunately, respect for the language, English or native, has diminished.

Social media imposes space limitations; text messages were never intended to be long to begin with; and emails too have gone the SMS way, with everyone wanting answers ASAP, seemingly having no time to say sorry and just enough to type SFLR (Sorry for Late Reply), and apparently not caring enough to appreciate another human with a heartfelt line or two, rather a generic YMMD (You Made My Day). You may not use these in your daily lingo but millions of internet users around the world do, and that can be pretty scary or amusing, depending on how you look at it.

Where before speed reading was about getting through the pages of books at a blindingly fast pace, today we ‘power skim’ online, picking up the essence of the story and giving little thought to the manner in which ideas have been expressed. The internet encourages us to read the headings, images, tables, graphs and bullet points in an article; whether or not we want to enjoy the writer’s prose is up to us. A long-form article, though appreciated, is expected to include captioned images, subheads, bulleted lists, statistics and tweetable quotes. And for readers who can spare less than a minute on an article, there’s always tl;dr (Too long; didn’t read).

 Language is still necessary for social interactions and critical for professional authors as well as the continuing existence of the publishing industry. What we’re witnessing is watered-down, filtered and sieved language. This is reflected in the general sentiment that learning ‘some’ English to land a job will do; in the process, we fail to become articulate at English and the approach often comes at the cost of our native language. Ultimately, we struggle to gain command over either language, missing out on its beauty and leading a colorless life on the internet.

What Can the Fibonacci Spiral Teach Us About Data Strategy?

A key component of a data strategy is the creation and efficient management of the data value chain. The data value chain can be understood as the information flow within the organization and series of steps needed to generate value from that information. The value chain can be split into the inbound processing pattern and the outbound processing pattern.

In the former, the data supply chain takes data from acquisition to analytics and consumption. Data is received from a variety of sources, checked for relevancy, enriched, analyzed and put to use to gain useful insights. Inbound processing describes the transformation of inbound data into information. The data supply is a six-step process:

  1. Identify and acquire
  2. Catalog and cleanse
  3. Extract, Transform and Load (ETL)
  4. Prepare for end users
  5. Management information and reporting
  6. Advanced visualization, predictive and prescriptive analytics

This process can be envisioned as a ‘Fibonacci spiral’ funnel that moves inbound data from acquisition through to analytics. Once it comes in, data is assigned measurable values on an index that evaluates its relative importance. The valuable data identified is then moved for consumption.

Figure: Fibonacci Spiral – Data Value Chain

From the image above, the consumption phase is at the center of the spiral; this is where the data is readied for end users, the phase where data achieves its greatest value by getting transformed to value-generating information that improves decision quality. Organizations should focus a majority of their IT dollars and resources here.

A good tactic is to invest in types of consumption that are best at transforming data into usable information such as predictive and prescriptive analytics and visualizations. These tools do an excellent job of providing high-value insights and information because they make data easier to comprehend. They make the processing of the information intuitive by telling stories, illustrating through graphs or charts, and prompting quicker action.

When information is understood easily, it will be leveraged by users to make strategic decisions. When it is a confusing mess, people will likely not bother making sense of it. This is why person-centric analytics is so important to your data strategy. Give users a reason to embrace information and there is no reason why they wouldn’t – after all, they will benefit immensely from it. To enable this, organizations must create an economically optimized infrastructure that allows efficient ETL, and manage data well at every stage.

Leadership Rules for the Digital Age


In a digital age where data is power, a company’s competitiveness depends greatly on its management’s attitude to data. The days of taking decisions within a ‘time window’ are behind us. Today, decisions are made in real-time. Decisions made using data are quicker, more precise and more effective than those with no data or outdated data. Which means the two important questions leadership should ask are:

 Are we making the right decision at the right time?

Has the company embraced changes to stay competitive in a technology-driven business landscape?

 For data to be available on-demand and in an easily comprehensible format, a robust data strategy must necessarily be in place. Part of that strategy is to identify tools that allow the company to make sense of increasingly complex data systems.

How best can we plan our technology investment to ensure that the tools we implement are delivering their true economic value?

 As data strategies and investments will be planned, controlled and overseen by executives, it goes without saying that companies need the best people in leadership roles. Data strategy is, after all, person-centric; the most powerful technology is useless in the hands of incompetent teams. What, then, are the rules on ‘ideal leadership’ in the digital age?


  1. Ensuring that the people who make data work are given the freedom and autonomy to state their views in key conversations. They should be empowered to act as leaders to drive innovations and positive transformations with data. These ‘datapreneurs’ should have representation in senior leadership, on the board, and at the managerial level.
  2. Datapreneurs’ don’t necessarily need to hold a formal leadership role but be regarded as influential, valuable assets whose expertise is called upon during strategic business meetings. Though they may be technical gurus, their value lies in aligning technology and business to drive new value. They are entrepreneurial first and technical if required. They are concerned with leveraging data in improving business processes and bringing everything together.
  3. You can pick datapreneurs’ from your existing workforce or hire entrepreneurial self-starters into key positions at every level of the organization. These individuals must be technically adept and entrepreneurial minded, with the ability to create, pursue and realize a vision. They must be able to identify opportunity in order to monetize data investment; influence and gain support in the ecosystem; raise funding for the effort; build a team to create and deliver the value proposition; and scale out the proposition to create lasting value.

How Data Consciousness Helps Create Value in The Digital Age



Like the vast ocean of consciousness that is the human mind, today, continuous streams of data flow from smartphones, computers, television, sensor equipped buildings and retail store cameras. Data is everywhere and its potential to understand human reaction is limitless. Already, many companies are exploiting this potential to gain a deeper, 360 degree understanding of their customers. I would like to refer to this as, ‘data consciousness’.

Big data recommendation engines are allowing Amazon and Netflix to make purchase suggestions based on interests and choices that customers have previously demonstrated. Some, like Target, have taken it a bit far to discover when women are pregnant by tracking purchases of unscented lotions, and used this information to offer loyal customers special coupons and discounts. The public sector’s use of big data has seen law enforcement predict the time and location of a future crime; biologists investigate gene pairs to determine traits such as resistance to certain diseases; and genomic analysts find links between air quality and health.

Data is valuable because it tells us stories in the form of information and insights. To maximize benefits from data, you want to know the whole story: where it begins, what happens and how it ends. This makes it imperative to manage and utilize data thoughtfully at every stage of its lifecycle.

IBM describes the lifecycle of data as a seven-step process:

  1. Create
  2. Use
  3. Share
  4. Update
  5. Archive
  6. Store
  7. Dispose

To understand how data gains life and begins its story, consider the data trail left by a guest checking into a hotel. The data is created the moment the guest checks in; it is then used and shared with relevant process stakeholders; after the guest’s departure, stored, updated and archived for future reference, and ultimately disposed off once it is of no use to the hotel.

Throughout its lifecycle, this data has the potential to create value for one or more business processes. In fact, once it is available, companies need to start putting it to work. For instance, insights on guest check-ins can be shared by third-party partners (this needs to be stated in the Privacy Policy/Terms&Conditions, and accepted by the guest). When unutilized, the hotel or its partners could be missing out on revenue, loyalty, marketing, service improvement, monetization or other business opportunities. Most often this occurs when the data is never used (‘dead data’) or disposed off too early.

Smart organization and lifecycle planning of data can help companies stay one step ahead of their customers, anticipating their reaction to an offer with a high degree of accuracy, and taking timely actions that deliver business value.

‘Data consciousness’ of the lifecycle of data itself, helps create value in the digital age – through superior business practices.


Three Ways To Revolutionize Healthcare Industry With Big Data

For the enormously complex healthcare system, big data is an invaluable asset with immense potential. Big data use cases in this sector are many and varied. In this post, we will limit ourselves to three, with special focus on data security.

Combating data breaches

2016 witnessed 106 major healthcare breaches, which resulted in the exposure of approximately 13.5 million records. Multiple data breaches at the same organization are all too common. Cyberattacks have become increasingly brazen, which is pretty counter-intuitive given the vast arsenal of cybersecurity solutions available today. Healthcare organizations that treat data breaches as just another IT issue risk losing trust and authority among the public. Inability to manage customer data reflects poorly on organization’ commitment to patient well-being and makes them appear callous and insensitive.

Big data is enabling organizations to analyze and control cyberattacks in ways not possible before. For instance, big data security analytics that heuristically analyzes security threats, can serve as the first line of defense against cyberattacks alongside human oversight and a powerful reporting engine. It analyzes unstructured data and extracts information that you wouldn’t have even thought of looking for.

Healthcare organizations need to apply a data perspective to security to put more effective controls in place. One approach would be to place controls around business processes that hold the most valuable and sensitive data. This can be enabled by examining data patterns and visualizing as well as effectively managing the organization’s data value chain. For organizations that cannot afford to deploy expensive security analytics software, smarter data-driven analyses can help identify processes that require the highest protection so more resources can be allocated in a targeted manner.

Gleaning insights from patient information to improve quality of care

Healthcare providers are integrating health care records across their hospitals and medical offices to build an integrated and comprehensive data hub. The goal is to leverage data at micro and macro levels, capturing insights that aid the delivery of more personalized patient experiences, improve outcomes and decrease costs.

Improving pharmaceutical R&D

Pharmaceutical companies have hastened their pace of zealously pursuing blockbuster drugs. Not every high-potential drug can provide the envisioned financial benefits. Sometimes, they come at the cost of less profitable but important drugs that are required extensively in certain parts of the world (ex : drugs for treating dengue fever in developing countries).

Big data and predictive analytics can help the pharma industry make informed assessments on which drugs have the highest probability of success, and allocate their investments more efficiently as well as responsibly.

Engaging Your Employees in a Digital World


A lot of the conversation around the digital workplace has focused on the impact of  robotics and artificial intelligence on human jobs. On the one hand, there is fear that automation will make human talent disposable; on the other, there is optimism around the innovation that human assets will be able to lead when robots take over repetitive tasks.

At first glance, changes brought about by the digital age may seem threatening. On closer look, you will realize the unlimited potential it offers – for the enterprise and its people. It is only when the  enterprise can frame a people strategy that communicates an evolving business model aligned to the digital age can employees gain a clearer, logical view of key players and participants.

Sure, there may be robots too, but people will always be there. That’s because one of the chief goals of digitalization is to gain insights and identify patterns aiding the creation of products that serve PEOPLE better, empower EMPLOYEES to think innovatively, and create better CUSTOMER experiences to encourage loyalty and customer lifetime value.

In other words, ‘going digital’ benefits people (customers) and is for the benefit of people (employees). The new digital, data-driven business model is, in fact, designed to serve its shareholders. What platforms and tools are enabling is the easy access to data, which is organized and structured for easy searchability and quick, on-demand retrieval. Data repositories in the digital world are ‘person centric’, whether that person is the customer, supplier, employee or business partner.

To be data-centric is to be person-centric; a data strategy will always have elements of people strategy.

Take Airbnb : the online hospitality service uses machine learning and big data to help hosts find prices that earn them (and the company) more money. Data is being leveraged to improve decision-quality – that could mean better productivity or more exciting projects for employees just as it has translated into higher incomes or better deals for other stakeholders. That data is the new gold is also being acknowledged by traditional incumbents in such sectors as telecommunications and insurance. New or allied business models are being conceived to extract the potential of data in strengthening capabilities and enlightening customers.

When digitalization is viewed through the humane lens of ‘serving people’, it is bound to be welcomed instead of feared by employees. ‘The datapreneur’ who can champion digitalization of processes without disturbing the core value that employees cherish, will be able to encourage the enthusiastic adoption of both the advanced tools as well as the mindset needed to excel in our rapidly expanding digital world.




Why Data Mis-Management Is Killing Your Business, In The Digital Age.

Most companies and their leaders, acknowledge that we are now in the digital age.  In many sectors though, the strategies and subsequent actions don’t reflect this reality.

Data still appears to be miss-understood.  Especially by large financial institutions, such as banks and insurance companies.

For a company to be considered digital, their business model needs to be digital in nature. i.e., their business processes are digital in nature. Data in general, is the reflection of the business process, which in turn is a reflection of the business model, which happens to be a reflection of the business strategy itself.

Fig: How Data Intersects with Your Business Model and Business Strategy

While companies that consider themselves to be ‘digital’ enjoy higher valuations, companies which don’t, struggle with their valuations.  By digital, we mean companies whose business processes are enabled digitally.

The classic example that I tend to share is the valuation of Paypal as compared to some banks.  The banks off late becoming less valuable in market capitalisation terms as compared with younger companies.

What is this disruption? Can data management help?

Companies that are able to successfully connect the dots between their business strategy, business model, business process and finally, their data landscape are the ones that are wining and will win in the digital age.  The rest are likely to disappear. They will not survive.

When data is managed with an intension to create real economic to the shareholders, the customers, the employees and other relevant partners in the business model, there is highest probability to create massive value in financial terms, as the data in these circumstances is likely to be ‘person’ centric in nature.  Here’s where emerging technologies related to big data analytics and cloud play a crucial part, in helping shape the business strategy execution.

On the other hand, when leaders do not recognise this, there is a tendency to miss-spend, delay or ignore timely investments and actions.  Which, as we know will end up in disastrous consequences.  Drop in revenues, in ability to develop the right value propositions, inability to efficiently manage business processes and operations, in ability to manage talent and the list goes on.

What matters most in any enterprise, is their ability to fit into the ‘digital world’ in a manner that is worthy of being able to serve the people involved in the business processes, adequately enough, and where possible, delightfully!

Ignore data at your own peril. Manage it to create value and you have a chance to win in the digital age. Nurture what I call, ‘the datapreneurs’ in your organisation.  They will know how to create value in the digital age.