D. Justhy's Blog

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The Importance of Having a Simple Data Strategy

The notion of an overarching enterprise data strategy can be quite overwhelming. You can start by having a clear vision of the desired business impact from data. Specifically, on how data will be sourced, how it will be utilised and how organizational transformation(s) will eventually be enabled. All this must be clearly understood. Many more questions could emerge as well. Such as the following:

– How will we identify, combine and manage multiple sources of data? Regardless of (your) industry or size, you can expect datasets to be large and complex.

– Do we have/How can we build the capability needed to utilize tools and/or build methods that optimize enterprise outcomes?

– What kind of leadership initiatives and attitudes can transform the organization so that the tools and methods actually facilitate better, more consistent as well as profitable decisions?

To make data a fabric of day-to-day operations, everyone must be aware of the data strategy being pursued by the enterprise, the value it is expected to deliver, and their role in solving problems and making the most of opportunities. In this scenario, simplicity, transparency and usability can help create a mind-set that is more accepting of data and helps adjust to a data-driven culture.

Why you need a data strategy blueprint

A strategic view of data in particular and technology in general is imperative to realize measurable business value. When framing the strategy, three parameters come to the forefront: commoditizing the data infrastructure, industrializing data integration, and ensuring organization-wide consumption of reporting, analytics and visualization capabilities that are both internal as well as external facing. With this line of thinking, you can bring your complete IT infrastructure together into a single blueprint.

A data strategy blueprint serves your enterprise as a compass to indicate the current location to where it should ideally be in the digital age. There are three steps involved:

  • Data serves the purpose
  • Data as a capability
  • Data as an asset

All three depict data at different stages of its value to the enterprise. In the first stage, data does what it was intended to do, nothing more. In the second, data starts driving results, impacting innovation and being valued across the organization as an integrated and a consistent capability. As an asset, data creates an environment of constant innovation and makes the organizational culture more purpose-oriented, whilst simultaneously creating massive economic value. The latter being the ‘holy grail’ for digital age success.

When operating with a strategic intent, the blueprint also offers the big picture on what must be done with data, describing how best to organize data across the enterprise landscape. The tactics and actions that must be followed through for each of the aforementioned strategic parameters can be more clearly understood with a blueprint. When the strategy is broken down into its constituent parts, it ceases to be complex. A ubiquitous strategy outlining the desired outcomes of collective activity is required to help datapreneurs and the people they lead understand the value of their contributions and empower them to excel, in the digital age.

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 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.

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.

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.

 

 

Three Little Known Factors That Dilute Your Enterprise Data Strategy.

Today’s enterprise data is critical for the success of traditional companies, i.e., those enterprises whose business models are not digital.

The trail that business processes leave over time, is the very data which enterprises must manage for various growth and regulatory needs.  All this of course, calls for the right data strategy. However, many traditional organizations are likely to struggle, with their data strategy articulation and the execution.

Here are three reasons for this struggle:

1] LACK OF AWARENESS

About eighteen months ago, a Chief Data Officer (CDO) of a traditional bank, was wondering why a data strategy is required for him to perform his new role.  In fact, the executive was convinced that the focus only needs to be on data governance related strategies and nothing else. Yes, no analytics and no big data! Sounds, familiar?

There was a sense of reluctance to even consider anything thing beyond just data governance, that too, the only the elements considered ‘mandatory’ from a regulatory perspective.  Enabling growth did not stand a chance in the CDOs agenda.

This indeed is lack of awareness of the value that could be derived from a sound enterprise data strategy.

2] LACK OF CAPABILITY

In certain cases, there is adequate expertise in the enterprise as well as an acknowledged awareness that a data strategy is required.  However, the sheer pressure of the business environment renders the organization incapable of any meaningful action.  A sense of uncomfortable ‘status-quo’ persists, while waiting for external factors to trigger an initiative.  An example is a large organization waiting to prepare for an IPO or a capital raise. Traditional companies under this category, perhaps have industry leading expertise on matters related to data strategy but they lack the capability to develop and deliver the data strategy, which by the way happens to be time sensitive.

3] INCORRECT APPROACHES

Articulating and executing a relevant data strategies is great, but may well be catastrophic if the wrong approach is adopted. Without the correct strategy, data is only a ‘potential asset’.  When, mishandled it erodes massive value in the form of opportunity costs.  This would typically run into billions of dollars and this includes the potential hundreds of millions of dollars in misspending.

If you happen to carry the unfortunate fate of a CEO, CIO or even a CDO or an organisation, that neither has an awareness, capability nor the ability to execute the right data strategy, there is still hope as in any ‘turn-around’ situation.

Since, 1955 more than four hundred of the Fortune 500 companies have disappeared.  At the same time, Johnson & Johnsons, Wells Fargo and General Electric, companies that were all founded in the 1800s still manage to be in the top list of most valuable companies in 2016.  The only thing that is common between them was that they had a data strategy that helped them transition into the digital age.

While traditional companies that ignore their data strategy are doomed, there always are those super stars that fight their way up to the top.

Let’s just hope that there are more super stars in the coming years.