Mobility – a promise of exciting possibilities in Exploration & Production

When was the last time you silently cursed a badly designed feature while using your email client, newly installed software at your firm, IVR menu of a call centre, your online banking system, your car or even your office coffee machine? Chances are it was last week —it’s another matter that you sucked it up and moved on with your life.

You could perhaps attribute your frustration to the ubiquitous mobile app (the poster child of the digital economy) for enhancing your sense of fine user experience. And you may not be alone in doing that as mobile apps quietly go about shaping many of the expectations we have from the products and services we consume on a daily basis.

In the last few years we’ve seen number of mobile apps skyrocketing to over 5 million  with over 90 billion downloads, yet users spend most of their time only on a handful of apps. This has not only intensified the competition for user mindshare, but also generated a vast body of knowledge and best practices for building quality apps that users love and want to come back to, again and again.

It is not hard to grasp why user experience (UX) can be a tough nut to crack for mobile apps. We expect them to be simple, elegant and yet highly efficient in getting the job done. Any app that is taxing on our thumbs (or fingers) and requires more cognitive effort than changing the channels on our TV can tick us off.

That said, not everyone is losing their sleep over the UX. Certainly not the Exploration and Production (E&P) sector, apparently.

UX in the E&P Sector

Just like any other digital technology, mobility is also creating certain excitement in the E&P sector with slow and steady uptick in app adoption. Quite expectedly, E&P CIOs along with their business counterparts have started putting together their firms’mobility strategy.

However, the main focus of the strategy has been around technical aspects such as data security, choice of enterprise mobility platforms and app development approach. As far as UX is concerned, it has yet to get much attention beyond the customary nods from the think tanks. It would be understandable if mobile apps were coming off-the-shelf, giving E&P firms little say in the UX design, but the majority of apps in the E&P sector today are custom-built (in-house or by service providers) and will continue to be for some time.

The general thinking is that the market offers enough expertise that E&P firms can summon to deal with the UX. True, but I also believe that external expertise will have limited value if the firms don’t have the internal capabilities and culture to absorb and complement the practices that mobility solution providers bring to the table.

Let me highlight a few such practices that merit attention from the E&P firms.

Design Thinking   

User experience is more than an attractive screen with fancy charts and colours as some tend to believe. UX is a greater whole delivered by various parts such as screen layout, controls, authentication, navigation and data access. Imagine a car, where a great driving experience is delivered by many parts of it such as: engine, powertrain, chassis, interiors and various utilities coming together – not just by the way the car looks.

E&P firms have traditionally relied on their super users or Subject Matter Experts (SMEs) to design their desktop-based applications such as Dashboards. While taking nothing away from the SMEs, it’s a lot easier to design an interface for the large dual screens with fast internet speed —that many E&P engineers enjoy —compared to the smaller screens of mobile devices on unpredictable internet connectivity.

An efficient app design demands deep understanding of the users’information usage patterns, working styles, environment and smart trade-offs in the features. A field engineer raising (often with the gloves on) request for corrective maintenance on a tablet requires a different user experience than a manager approving that request on a smart phone. There is simply no cookie-cutter approach to the app design.

Forward-thinking E&P firms, being aware of this challenge, are embracing modern design concepts such as Design Thinking that help build deeper insight into the business problems being solved by the app and generate ideas to create the optimal design by drawing upon end users’diverse points of view.

At the same time success of such methods requires acceptance and wider participation of end users, many of whom may be getting exposed to them for the first time and may not feel “at home”with terms such as Personas or Journey Maps. This is where leadership needs to play an active role in creating awareness and breaking internal collaboration barriers.

Iterative Development

Experts advise that getting the right user experience is an evolving process that entails multiple iterations focussed on making design enhancements by learning from end users’feedback on incremental software releases.  This philosophy of “do fast and learn fast”has led to the wider adoption of the Agile methodologies with DevOps becoming a de-facto standard for mobile app development in many hi-tech sectors.

Moreover, emerging cloud-based architectural paradigms utilizing micro-servicesand low/no-code toolspromise faster release cycles, shrinking them down to a few weeks or even days. They also allow the development teams to experiment with a variety of technology components to achieve design innovations.

Embedding these practices, however, will require E&P firms to rethink their IT project management and software development practices that are probably suited for “certain”types of IT projects but could encumber the mobile apps that tend to have smaller scope and lesser complexity.

For example, many E&P firms still struggle with Agile in their IT projects due to functional silos, particularly those involving business and IT, combined with the documentation and approval-heavy project management stage-gates. On the technology front, E&P firms, barring some notable exceptions, still show reluctance to use cloud based infrastructure for software development, which creates dependency on physical infrastructure availability and limits the project teams’architectural options for meeting the design requirements.

Mobile Product Management

Mobile product management is one of the widely recommended practices for ensuring that UX keeps pace with changing operational realities and technology landscape. It combines the processes, governance and product championship focussed on continuous improvement of the apps through harnessing market innovation and constant engagement with end users, gathering their inputs and funnelling those into the planned release cycles.

So far E&P firms haven’t had to worry about the product management. Their flagship desktop applications come from the vendors while custom-built applications, once developed, receive reactive maintenance from IT until they get phased out.

In order to protect their investments on mobile apps, E&P firms may have to consider putting in place the structure, discipline and skills to manage the apps like products. Leaving the responsibility for upkeep of these apps on IT and service providers’may not bring the necessary innovation to keep the apps relevant to the users.

WrAPPing it up

While some of these practices may not be news to the E&P firms, not many —especially those in the early days of their mobility journey —will show appetite to go through all the pains to adopt them just for the sake of UX. Some, I suspect, wouldn’t even mind a few hits and misses with their apps as they navigate the mobility learning curve. After all, a bad UX has never put any E&P firm out of the business and failures of a few apps here and there may not burn a big hole in the managers’pockets. End users, on their part, might just put up with the average apps like they have with the clunky dashboards, sundry applications built with Microsoft Access or Visual basics and interpretation tools that still require running command lines or scripts.

However, the optimist in me believes that things may look better once mobility takes hold in the sector, E&P firms become more digital and a new generation of the users (a.k.a. millennials) —spoiled by the Instagrams, Snapchats, Dropboxes and Google apps of the world —have significant voice in their firms’digital affairs.

What we would see then are not only great apps in the E&P sector, but user experience taking center stage in development of almost everything that goes into a computer screen with a human on the other side of it. That would not be just hugely empowering to the end users, but could transform work practices in ways unimagined so far.

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Mastering the art of analytics: a Groundhog Day story of the exploration and production sector

Being data-driven is the new war cry of the E&P sector these days. Feverish frenzy around Digital and highly publicized business successes of the data-driven firms from the other sectors are driving most of the E&P firms’ fascination with Big Data, analytics or IOT these days. E&P leaders or senior executives, on their part, are also coming out in support of wider adoption of analytics and need to do a lot more with their data.

But beyond all the intent and excitement, not much has changed to suggest that E&P sector is on its path to becoming data-driven. Here is why I think so and most of it is based on my last 12 years of observation of the sector analytics initiatives.

Decoding data-driven

While there is no universal definition of what being data-driven truly means, various publications and experts’ commentaries on the subject identify three main acts that define data-driven firms.

1: They manage their data well

2: They leverage teamwork and wide variety of data sources to generate insights

3: They approach analytics objectively, acknowledging and addressing the human biases

Let’s look at these in the context of E&P sector.

First Act

Act 1 needs no introduction having been a proverbial thorn in the E&P firms’ flesh and deservedly received significant attention, if not action, for last as many years as one can remember. Data gurus have warned us—and rightly so—enough about the perils of poor data quality in anything even remotely to do with analytics. So any further discussion on this will be preaching to the choir.

Second Act

In the world of big data, Variety, and not Volume, is what drives the business value of the analytics, according to the experts. There is a reason why it’s the case. New correlations from analyzing different data sources (both structured and unstructured) can create new knowledge of performance drivers—a key to insight generation. While Variety is gaining greater currency in the consumer-facing sectors such as retail, it’s not a common practice in the E&P sector.

I can think of two reasons for that.

First, data sources being analyzed could belong to multiple departments which, in many E&P firms, don’t naturally share data or collaborate with each other. This limits the opportunity to develop broader perspective into a business problem, necessary to explore a range of the performance drivers. For example, through collaboration with the technical teams, supply chain teams can develop better understanding of how different plant configurations, field characteristics or operational activity patterns influence the material demand, as opposed to only analyzing their own datasets.

Second is the unknown or poorly understood causalities in new correlations that are bound to show up when analytics involves multiple data sources. In a science-driven E&P sector, engineers are predisposed to look for causalities in the correlations in order to trust the analytics. So any correlations which engineers can’t explain with their domain knowledge are likely to be dismissed.

For instance, in equipment failure prediction analytics, operations teams feel comfortable exploring the correlations between the past failures and equipment performance data because causality is well established there.  But when it comes to exploring the correlations between equipment failures and, let’s say, weather cycles or workforce demographic patterns, causalities may be less obvious to the engineers. Such “unusual” correlations require deeper analysis and experimentation to verify the causalities and many E&P managers may not have the appetite, analytics acumen or resources (read data scientists) for such experimentations.

Third Act

Which brings us to the third act: navigating the human biases in the analytics.

We all have been told to mind the dangers of “garbage in, garbage out” with computers, but “bias in, bias out” could soon replace that advise in the age of Big Data.

Experts argue that knowledge gaps and individual incentives can create hidden biases in both the collection and analysis of the data compromising the analytics results. They also recommend addressing these biases through experimentation (ref to Act 2), research and training.

In E&P sector, given the typical data uncertainties and intuitive decision making styles, biases can occur naturally and are well documented in the SPE literature. Left unchecked, they can limit the adoption or quality of the analytics efforts especially when the latter are misaligned with the managerial incentives.

For example, in the E&P capital projects, pro-project sanction bias of the engineering teams is described as one of the main reasons for overly optimistic production forecasts and poor concept selection. In this scenario, any attempt to bring analytics to improve the accuracy of project evaluation is likely to meet resistance if it reduces the chances of project getting approved and in turn career advancement of the individuals.

Managerial incentives also rub off on their teams in the way they approach the analytics. Technical teams incentivized on oil gains from well intervention opportunities, may focus their analytics efforts on identifying expensive drilling and workovers targets than exploring cheaper production optimization alternatives on the surface. Operations teams compensated for meeting the production targets may sidestep the competing advice from equipment predictive model if it incurs production loss. The unfortunate BP Macondo incident is an example of this behavior where the rig staff, under pressure to make up for the lost drilling time, reportedly misinterpreted the negative pressure test.

Groundhog Day Story?

Many would agree that mastering these “softer” aspects of analytics is as critical to being data-driven as harder aspects such as skills or technology. However, in my experience, “soft” is seldom acknowledged, much less addressed, in the analytics initiatives in the E&P sector. Many E&P firms still tend to approach analytics as a tool to solve tactical—and often one-off—problems than a philosophy of doing things. This mind-set has led to analytics projects being IT group or departmental endeavors than leadership-driven initiatives.

It’s not hard to see why so many sector analytics pilots after promising starts have failed to deliver. Without hands-on and committed leadership involvement, initial momentum from successful, if at all, pilots is lost as soon as the projects run into the issues described above.

Are the things looking any different on the front line? Not much I’m afraid.

E&P workforce, especially the earlier generation, still identifies analytics with the first principle methods such as reservoir simulation or engineering models while viewing data sciences based methods (e.g. statistics, machine learning) with skepticism. The current crop is more open to the latter but has a steep learning curve to climb in large part due to the absence of formal data sciences learning programs in the sector or academia. And while conferences are good forums for knowledge exchange, E&P analytics conferences are fast losing their novelty. Once you have attended a few, you have probably seen it all.

In one such conference, sitting in a big data session, I overheard a couple of participants sigh and mutter “the same old stuff!” Another one at the end of the session called the whole event as Groundhog Day in an obvious reference to a popular movie (with the same title) where the lead protagonist is forced to live the same day over and over again. Even though ironic, I thought that was an apt summary of the current state of the analytics in the E&P sector.

But let me end with a note of optimism. In the movie, at the end of the day (literally), hero emerges transformed and enlightened. I hope E&P sector ends its Groundhog Day on the same note and emerges truly data-driven.

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Expanded Network Isolation and IAM Updates for IBM Watson Services

Today, we continue that progress by announcing full support for IAM with Watson services and better support for network isolation.

Full support for Identity Access Management

API keys for Watson services are no longer limited to a single service instance. You can create access policies and keys that apply to many services and can grant access between them.

To support this change, the API service endpoints use a different domain and include the service instance ID. The new URL pattern is as follows:

api.{location}.{offering}.watson.cloud.ibm.com/instances/{instance_id}

Example of new Watson Assistant API endpoint hosted in Dallas, Texas:

api.us-south.assistant.watson.cloud.ibm.com/instances/6bbda3b3-d572–45e1–8c54–22d6ed92r32q

Beginning in December 2019, you will start to see these new URLs as you create service instances or when you add service credentials.

But, don’t worry. These URLs do not introduce a breaking change because they will work for both your existing service instances and for new instances. The original URLs continue to work on your existing service instances for at least one year.

Public-private network endpoints

As enterprises move their workloads to the public cloud, how you secure access comes into focus. Premium Plan users will gain access to both public and private network endpoints. Connections to private network endpoints will not need public internet access.

Private network endpoints support routing services over the IBM Cloud private network instead of the public network. The addition of private service endpoints will provide your applications with the following:

◉ Instance-specific private URLs, reducing the amount of network traffic traversing over a public network.

◉ Reduced cost with unlimited inbound and outbound traffic on the private network—there is no egress when the traffic flows over a private network endpoint.

◉ Reduced latency and increased security for traffic to Watson Services as data stays within the IBM Cloud network.

Enabling private network endpoints

Users looking to adopt private network endpoints will need to make sure they are on the Premium Plan for their Watson service. As seen in the GIF below, a user can switch between public and private endpoints in three easy steps.

Step 1: Enable Virtual Routing and Forwarding on your IBM Cloud Account.

Step 2: Navigate to your service instance that is on a Premium plan and click the Manage tab.

Step 3: Click Add private network endpoint link to enable access to a private service URL.

Current Premium users can make use of Private Endpoints in their existing service instances.

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How Fortune 500 incumbents can become the new market disrupters

According to the 2018 IBM Global C-suite Study, the rules around market disruption have changed. Until recently, small startups and industry outsiders were widely considered the primary drivers of innovation. However, the results of the IBM C-suite Study clearly show that today, this role is more commonly filled by large, established companies.

This shift creates both an opportunity and a challenge for these industry incumbents: many of their peers have already learned how to be the disruptors in their industries, and now the possibility exists for these incumbents to do the same. Disruptive innovation requires businesses to act, think, create and engage differently. And in a business world measured by the tweet, companies that don’t take the proper steps will risk falling hopelessly behind.

Grow smarter, faster and more cost-efficiently by taking three critical actions

In order to transform for success in the digital era, there are several core steps an organization must execute against. Three of these actions were echoed at the IBM CEO Big Bet session:

1. Take out structural costs to increase competitiveness and fund investment in growth.
2. Transform enterprise processes and systems to enable growth and competitiveness.
3. Innovate and use data as a basis for new growth.

Taking out structural costs

For incumbents to capitalize on new opportunities, transform their processes and become the disrupters, they must have funding available when opportunities arise.

By modernizing existing technology, incumbents can take out structural costs, freeing up resources to invest in growth. As a first step, many incumbents who use Oracle solutions are being assisted by IBM to move out of their data center(s), migrate to a flexible Oracle Cloud IT platform, and transform to an SLA-based delivery model. We do this through our premier offerings for Oracle ERP and HCM on Cloud.

In addition to cost savings, the Oracle Cloud solutions also provide operational improvements and financial flexibility. Firms can then experience improved availability, security, performance and reliability of their IT operations. From a financial flexibility perspective, these incumbents transform their balance sheet by shifting their expense profile from CapEx to OpEx, giving them the liquidity they need to grow.

Taken together, these benefits enable incumbents to pursue new ventures, partnerships and alliances, reimagine how they interact with customers and reshape their industry. Incumbents can also pivot and change quickly with Oracle Cloud solutions by removing high up-front internal costs, system complexities and system constraints.

Transforming processes

After incumbents have removed structural costs, they are free to transform internal and external processes. Then these firms can harness the power of cognitive computing, robotics and automation to change the way they interact with employees, vendors and customers.

They can pursue new opportunities such as digitizing and creating a touchless financial close process, deploying a self-healing/self-learning application management service, or implementing customer and vendor solutions that create authentic individual experiences using virtual agents. Building and deploying Oracle Cloud solutions gives the incumbents a flexible platform to quickly transform their business processes and enables them to become a disruptor by capitalizing on changes in the market.

Fueling new growth with data

Finally, incumbents have years of proprietary data that can be used to create a competitive advantage. Unlocking this data with new Oracle Cloud solutions creates the opportunity for incumbents to deeply understand their customers on an individual basis, see the trends of their market before the competition does, imagine new ways of conducting business and introduce new services on an industry basis.

This private incumbent data — when paired with a new Oracle Cloud architecture, business processes and greater free cash flow — can give incumbents a newfound competitive advantage. By pulling business insights from their data, incumbents can make evidence-based decisions about how to personalize interactions with customers, identify which growth opportunities are most promising to invest in, and prioritize these opportunities based on facts not available to new entrants or the market. Essentially, incumbents are now in a position to reimagine and redefine how a market can be served.

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Navigating the hype of Artificial Intelligence- lessons from the trenches

I have been tracking AI (Artificial Intelligence) initiatives, for the last few years, across the business sectors through conferences, readings, personal experiences and my network. There is a distinct pattern about the approaches firms adopt towards AI. Some firms jump headlong into their AI initiatives, letting the chips fall where they may. On the other end, some stay too long in limbo, trying to work out every possibility before they take their first step. Evidences suggest that both the approaches produce underwhelming results; the former leads to the false starts while the latter delivers the over-wrought initiatives, stumbling along, till they finally get killed.

But then there are quite a few firms that manage to find the sweet spot between over exuberance and over prudence and not surprisingly, they also tend to have, as it were, better running AI projects.

As AI fever grips more E&P (Exploration and Production) firms and more E&P executives approve AI initiatives, they need to find their own sweet spot to get the best out of their pursuits. This is not a trivial task considering a hyperactive market fueling the AI frenzy and blurring the line between hype and reality. Moreover, AI is still at an early stage in the sector and there is limited appreciation of the risks and challenges that go with its implementation.

If you are a manager or functional lead nominated to oversee an AI initiative in your firm, you may already be feeling the pressure of managing the high expectations while delivering a successful outcome that will jumpstart your firm’s AI journey.

Assuming that’s the case, you may consider some of the following lessons, from the battled-hardened AI warriors, that could help you put your project on a sure footing and improve the odds of success.

1- Get acquainted

Over the year, the term AI has taken a life of its own and now become a catch-all phrase for many things such as machine learning, machine intelligence, deep learning, various forms of analytics (descriptive, predictive, prescriptive etc.), Big Data, IOT or even Industry 4.0. While some of these are just different terms for AI, some are far from it. The fact that not many can tell the difference has created wrong notions about what qualifies as AI. It has also given opportunities to the vendors to package their traditional analytics and pass it off as AI. The risk it that creates is that firms may start a project believing it is AI when it’s not. Or they may target the wrong problem to solve with AI.

It’s good if you are already up on the learning curve, but if not, then it’s not too late to take a crash course in AI—not with an intent to build the neural networks but to familiarize yourself with how it works and what its limitations are. This will put you in better position to see through the hype and ask the right questions to the vendors.

There are plenty of avenues for you to tap into, such as academia (e.g. MIT, Stanford) courses in public domain, YouTube videos or free awareness workshops offered by the vendors. Firms with their own data scientists or digital officers will obviously be more enlightened, but if you don’t have those, you can form your own team of AI enthusiasts who can be the voice of reason for your firm.

2- Start quick

Any digital transformation consultant will tell you about the value of right strategy, process design and roadmap before you press forward. They are all important but can also slow you down if it takes the consultants months to produce a bunch of PPTs and word documents.

Truth is there will never be a perfect strategy or roadmap with AI as things will evolve constantly and new knowledge will emerge in terms of what technology can do (or what it can’t do). Instead of spending too much time on a months-long strategy piece, it’s more effective to hit the road with a couple of ideation workshops, involving the right stakeholders. Target 3-5 opportunities to get the fundamentals of a 2×2 matrix then press go.

Focus on identifying the potential use cases and a candidate for the pilot project. Build a high-level roadmap and refine it as you go along. It’s normal if you notice skepticism from some people about the quick and dirty approach. You are better off showing value through something that works than “costly” PPTs. So deliver “that something” quick and let the results speak for themselves…which brings me to the next point.

3- Start simple

A typical pitfall in AI initiatives is to try to take on too much or start with a complex problem. Every AI project is an experimentation and experiments fail sometimes. Nothing hurts like a bloated AI project that goes on for months and then fails to deliver.

Starting with a simple scope helps you quickly test the feasibility of the use case while minimizing the potential losses should there be a failure. On the other hand, success will get you on firm ground and help you sell your initiative better. It also will give your team the necessary confidence and momentum to keep going. If, at all, your use case must be complex, break it down into phases and make the first phase simple. If you can’t deliver your first phase in a few weeks, then it’s still complex.

4- Take emotions out of your business case

Watch out if you are feeling too high about your initiative or acting under unrealistic urgency. Blind spots can creep in when stakes (personal or organizational) are high.
Problems that may follow can range from force-fitting AI into the business problems that are better solved using alternative methods, to selectively picking up the data points to build a business case, to even overestimating the business benefits.

You may think none of the above apply to you and you have an airtight business case, but you never know. It wouldn’t harm you to get a cold-eye review by the people who are still not infected by your enthusiasm. Let them chip away at the business case till you have whittled it clean of all the unknown assumptions. If the numbers don’t add up, iterate and re-examine or reconsider.

5- Set the right expectations

With so much buzz around AI, your stakeholders may unwittingly form very high expectations, especially when they are not familiar with how the technology works. On top of that certain individuals may have different views on what a project will deliver. This creates potential for dissatisfaction with the project outcome.

You must educate your key stakeholders on the expected project outcome and what success will look like, and communicate it at regular intervals. Have an open discussion with your AI vendor and agree on the success criteria. Surface all the assumptions, risks and dependencies, in unambiguous terms. Insist on the vendors to bring in their subject matter experts so that you get informed opinions and not just a sales pitch.

6- Get your best SMEs on the job and listen to them

A common wisdom in the E&P sector is that involving SMEs (Subject Matter Experts) too early or too much slows down an initiative. It’s not completely unfounded. SMEs either are too busy with their daily jobs or don’t share the enthusiasm of the project sponsors.

There is no getting away from the SMEs when it comes to the AI initiatives, nor should there be. SMEs are not just the lifeblood of the firm, but also their best ambassadors. Make them part of you project from the word go. Only they can help you validate the use case, build the business case, assess data quality, train the AI system and “nurture” it once it’s live. And not just any SMEs but your best and most experienced ones—an algorithm is only as good as the training it gets. Although it may create some change management issues early on, once you clearly communicate the purpose and value of the initiative, your SMEs will come to the party.

Be open-minded when your SMEs have opposing viewpoints on a use case. If they don’t see much value, then probably there is none regardless of what you feel about it. It’s time then for you to move onto the next use case.

7- Secure leadership commitment

Enthusiastic talks and rousing speeches about the technology in the meetings or the conferences are necessary but not sufficient evidence of leadership commitment. If the senior executives are not on governance board of the project or actively involved in it, you may ultimately face challenges. While things may go well initially, you will invariably come up against the roadblocks such as not getting data, access to systems or resources. Having leadership behind you will help you clear the obstacles faster than without them and save yourself a lot of time.

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Transform Your Data with Multicloud and AI

Make your data more accessible and valuable with the right cloud data management solution.

The cloud data management market has seen a significant amount of development toward multicloud and artificial intelligence (AI) solutions. Yet, sifting through the clutter to find the most valuable solution remains a challenge for many companies.

To help overcome that challenge, there are several considerations businesses should keep in mind when adding new clouds to their data management infrastructure. It is also important to seek out the right capabilities from providers who offer AI in their data management solutions.

It’s a hybrid, multicloud world

Offering deployment options on other clouds helps businesses develop applications with their technology of choice, even when they have standardized on a different provider. While this mitigates vendor lock-in, it adds a layer of complexity. Because so many providers are moving toward multicloud, your business needs to form a strategy around what cloud solution to integrate into its stack of solutions. Consider the following while shopping for the ideal multicloud data management solution:

Hybrid integration and enterprise performance

Organizations need to expand their thinking beyond cloud when looking for a solution. Optimizing analytics and application development requires not only choosing the best technology, but choosing the best deployment as well.

A data management solution that will operate seamlessly across on-premise, private cloud, and public cloud environments is, therefore, necessary. One way to promote this seamless integration is to choose a family of data management products built upon the same codebase, no matter where they are deployed.

Security and performance on cloud must also approach the level of its on-premise counterpart to help ensure high availability no matter where the technology sits. A good example is Db2 on Cloud, which not only has an on-premise and hosted option, but can deploy on both IBM Cloud and AWS as well.

Data transfer fees

One of the primary reasons many businesses use the cloud is cost efficiency. Many cloud providers tout their low-cost options, but businesses must investigate further to uncover all of the expenses incurred. Data transfer fees—whether cloud to on-premise or cloud to cloud—can quickly add up for enterprises as they conduct analytics. It’s best to look for data management options available on clouds that don’t charge these fees, such as IBM Cloud.

Migration support

Moving your data to the cloud for the first time can be a cumbersome process if a well-thought-out strategy is not put into place. Working with the right cloud vendor is necessary to make sure the migration is simple and fast.

Two primary types of migration should be considered: on-prem to cloud and cross-multicloud migrations. On-prem to cloud migrations must prioritize security and uptime. Failing at either could prove disastrous for a company’s bottom line due to lost productivity. Services such as IBM Lift CLI, with zero downtime and encryption of data in motion, set the standard.

Migrations between clouds in a multicloud environment must also be considered. If this data is not migrated quickly and easily, the benefits of having multiple integrated clouds can quickly break down.

Infuse your data with AI

Alongside multicloud adoption, modernizing information architecture for artificial intelligence has become a business imperative. Cloud data management solutions should be infused with AI to help businesses predict and shape outcomes by improving query performance and simplifying AI application development. In other words, they should be powered by and built for AI.

Solutions powered by AI will improve query speeds with machine-learning-based optimization of the routes queries take to data. They will also improve precision with confidence-based querying, which returns results based on predicted accuracy as determined by historical data.

Solutions built for AI fuel application development for AI initiatives by making it easier for developers and data scientists to perform their jobs. This includes support for popular languages and frameworks like Go, Ruby, Python, PHP, Java, Node.js, Sequelize, IBM Watson Studio, and Jupyter notebooks. The ability to perform complex modeling and visualization should also be available.

A good example of a data management solution that combines “powered by” and “built for” AI functionality is IBM Db2 11.5. Hailed as the AI database, the features present in Db2 11.5 extend to the entire family of Db2 offerings, including cloud options and the data warehouse. So, businesses that are ready to build predictive models and improve various business processes can train and run machine-learning models directly in the Db2 Warehouse on Cloud engine with no data movement or new skills required.

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What if you could operate 10x faster at half the cost using cognitive RPA?

In a hugely competitive global industry, telecom operators must balance ongoing customer satisfaction against reducing operating costs. Too often, subscale technology investments are made for meager benefits, and automation is bolted on cumbersome processes supported by decade-old systems. Worse still, automation is often implemented without revisiting the underlying customer experience or evaluating what artificial intelligence (AI) could do to improve productivity.

The telecom industry runs rife with highly manual, voluminous, repetitive and complex rule-based transactions – things such as order validation, service fulfillment, service assurance, billing, revenue management and network management – and closely coupled with multiple legacy systems. With as result rigidity and lack of transaction visibility. In traditional lead-to-cash processes this has often led to poor service and customer dissatisfaction. Slowed by the process, customers are disincented to stay loyal, looking for a better offer elsewhere.

Reimagining the customer first

Companies are increasingly using robot process automation (RPA) to automate routine tasks. RPA’s potential benefits are manifold. They can include reducing costs, lowering error rates, reducing turnaround time, increasing the scalability of operations and improving compliance. By moving beyond basic robotics to intelligent interaction by combining RPA and cognitive technologies, telecom operators can replace tedious tasks and deliver costs savings and greater workforce productivity by:

◉ Striking a better balance between the front and the back office, while becoming faster and more reliable

◉ Enabling customers to self-serve so that sellers can focus more time on complex orders

◉ Cross-selling and up-selling through assisted sales and creating recommendations that all sellers know what the best sellers do.

The integration of cognitive technologies and RPA (see Figure 1) is extending automation to a new level, in this way helping telecom operators to become more efficient and agile and delivering more consistent services to the customer which is paramount in the current digital economy.

Figure 1. Moving beyond robotics to intelligent interactions

Entirely new user experiences can be achieved by taking an over-the-top (OTT) approach. The idea is to preserve the legacy system’s (also known as systems of record) capabilities to be the custodians of the business transactions. By interfacing with the “systems of record” through existing APIs or microservices, one can redefine the user experience more freely and use a combination of capabilities ranging from business rules engine, business process and management (BPM), AI, RPA and blockchain.

Using these tools in conjunction, and playing to the strengths of each of them, business benefits are amplified beyond what is achievable by overextending a single technology. This enables transparent and flexible automation of the business process in response to business needs. Technologies embedded with RPA can provide autonomous decision making, enable reasoning and remembering, and provide new insights and data discovery. For example, using AI and RPA technologies as part of a sales order management process can guide the seller to improve data accuracy by making recommendations that improve over time.

A process automation platform that sits “over the top” of existing IT interacts with IT but doesn’t require significant change. It lets telecom operators design the customer experience they want, then implement the transformed process to support that experience. Since intensive manual intervention isn’t required, the benefits of creating the process and experience supported by a process platform have positive impacts on productivity, cycle time, cost and customer satisfaction.

Massively reducing operational expenditure

Faster order fulfilment at half the cost, more self-serve options so sellers can pay more attention to complex orders and intelligent lead-to-cash processes that steers the back-office towards more areas of value are key to change telecom (see Figure 2, a tier 1 telco).

Figure 2. Example of operator transformation business case

Dramatic cost reductions can be realized in incremental sprints, with significant meaningful change possible in just months. It requires focus on the following areas:

◉ Build a business case with the line of business or shared services that will see the greatest gains and align incentives to make collaboration happen. Start with relieving pain points and improving the user experience.

◉ Decouple legacy from a new user experience. Don’t rebuild IT but breathe new life into interactions with older, legacy applications to provide the much-needed budget relief.

◉ Create a center of competence that includes design thinking approaches and build AI and robotic content libraries and extensive industry-specific process flows and business rules. Consider AI process platforms to automate perceptual and judgment-based tasks through the integration of capabilities such as natural language processing, machine learning and speech recognition.

We’re at a key inflection point, moving from a world of processes run by humans supported by technology, to processes run by technology supported by humans. Where are you in seizing the opportunities that cognitive RPA offers?

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Three ways to collaborate to improve cybersecurity

The stakes are high in enterprise security. Data breaches can damage your organization’s reputation and result in significant costs (USD 3.86 million for every breach on average according to this Ponemon Cost of a Data Breach study). They can also destroy customer trust. Recent research has found that more than 78 percent of customers would not automatically return to a business following a data breach. In short, data breaches are just bad for business.

You’re likely aware that data breaches impact the whole organization. All enterprise systems are potential cyberattack targets, and the negative impact of a breach can reverberate throughout the business. Whether you’re in security, IT, or operations, data security is your concern.

Collaboration enhances data security

When it comes to enterprise data security, you may find it challenging at times to connect the dots. If you’re in security, you need information about the IT solutions required to secure the data perimeter. If you’re in IT or operations, you need insights from your security counterparts to inform technology development and deployment.

Collaboration can bridge this gap. IT and security groups can work together to ensure that security needs are baked into IT initiatives, and that security issues are optimally addressed by technology. By collaborating closely, your two groups can maximize transparency and make the best security and IT decisions.

Here are three ways security and IT can collaborate to enhance cybersecurity.

1. Consider security needs in technology development

If you’re a security practitioner, you’re plugged into the most urgent and relevant security concerns. You also understand how these concerns impact the enterprise. If you’re an IT practitioner, you’re aware of these issues and that they may impact applications you build. You can incorporate security peers’ insights into your IT projects to ensure your initiatives address all potential data-security risks and mandates.

For example, the recently enacted GDPR standards apply to virtually any personal data gathered by an enterprise that does business with or in the European Union. Before developing a new program that will use or request customer data, you must ensure that the program complies with GDPR mandates. Involve your security peers as early as possible here. Their early insights will help ensure that GDPR compliance is built into the application, not tacked on as an afterthought. A little collaboration at the start can save you a lot of headaches later.

2. Use IT to solve security challenges

The solution for an enterprise data-security challenge is often technology. This creates a natural synergy between security and IT practitioners. If you’re looking to address a data-security concern, one of your first conversations should be with your counterparts in IT. Often they will have the hammer for your nail, or they will be able to build the hammer.

Say you’re a security practitioner and your CISO has informed you that only a small portion of your enterprise data is encrypted. You probably both know, as the Breach Level Index has detailed, that unencrypted data is significantly more likely to be stolen by cybercriminals. Since expanding data encryption will likely require technology, you should then meet with your IT counterparts to discuss a solution. Perhaps they can find a way to devote more computing power to encryption so that a larger percentage of data – or at least the most sensitive data – can be encrypted. Ideally, they will be able to efficiently encrypt all database, application and cloud enterprise data through the mainframe.

When pondering your most vexing security challenges, make a discussion with your IT and operations counterparts a priority. They’ll often have just the tool you need to get the job done.

3. Reframe security conversations

It can be tempting to view security as the naysayer of the business, always warning about what could happen or what should not be done. Such a view may steer some IT practitioners away from engaging with the security team as they should.

Security conversations don’t have to be negative. You and your security counterparts are responsible for making them productive and positive. Discussions should focus less on how security concerns are holding business back, and more on understanding risks and alternatives. For instance, as mentioned earlier, in the age of GDPR security practitioners will likely raise a red flag about any application that collects and uses customer data. This doesn’t mean that the application can’t be developed or even has to be drastically changed. The developer simply needs to make sure that processes for collecting, using and storing this data comply with the mandate. IT and security practitioners should work together before development begins to outline a process that is compliant without compromising user experience.

A final thought: Stay informed

Enterprise security is everyone’s job. Accounting for security in technology development, and the other way around, will create an ongoing positive feedback loop in which security is woven into the enterprise needs and solutions.

If you’re a security practitioner, you’re already living and breathing security, but some time with your IT counterparts can help inform your security strategies. If you’re in IT, consider investing some time in cybersecurity education. You don’t have to become an expert. But you should be plugged in on the latest security issues, from the most recent high-profile data breach to any new data regulations. SecurityIntelligence.com provides news and insights that keep you in the loop on today’s critical data security issues.

Collaboration, supported by a base of security and IT knowledge, will help ensure an engaged team, improving cyber security for your enterprise.

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Source: ibm.com

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IBM z15 sets a new cloud security standard

On September 12, 2019, in New York City, IBM set a new and very impressive standard in the protection of data. With the launch of the IBM z15, IBM Data Privacy Passports technology builds upon pervasive encryption to help clients protect and provision data and revoke access to that data at any time from any location — even for data not housed on the z15.

This solution embeds data security policies and encryption with the data, enforcing data privacy by policy across the whole of your enterprise, even when that data leaves your data center. All this happens on the same platform that enables you to use hybrid cloud services, modernize z/OS applications in place and integrate with Linux apps on and off premises.

Protect data and ensure privacy

Pervasive encryption enables customers to encrypt data at the database, data set or disk level. The most crucial benefit of pervasive encryption is that it does not require customers to change or adjust applications. Each app contains an internal encryption-decryption mechanism, allowing clients to apply cryptography without altering the app itself. These functions go a long way towards addressing data protection and privacy management challenges that often arise throughout an enterprise transition to the use of hybrid IT and composable infrastructures.

Composable infrastructures deliver compute, storage and network resources as services from multiple logical resource pools. The approach treats infrastructure like applications. It gives technology teams the ability to construct new systems using software code to manipulate collections of software-defined building blocks. Infrastructure automation tools are used to provision required infrastructure on demand.

Achieve encryption everywhere with IBM z15

Security and data protection is a significant operational challenge for enterprise IT organizations. The IBM z15 meets this challenge through a variety of data-centric audit and protection mechanisms:

◉ Ability to track the location of all your data and status of all applicable security mechanisms;

◉ Capability to build data protection and privacy into all applications and data platforms instead of relying on an assortment of third-party tools;

◉ Security of having data protection and privacy controls embedded into every layer of the computing stack;

◉ Reliability of having a consistent identity management process in place across your hybrid cloud environment;

◉ Predictability delivered by consistently deploying all computing platform elements;

◉ Flexibility to securely move data between composable infrastructure components and third parties; and

◉ Comfort enjoyed by being able to meet new data privacy regulations and data sovereignty laws without fearing risk and economic loss associated with data security and privacy failures;

Secure your hybrid multicloud

According to the 2018 Ponemon and Opus annual study on data risk, 59 percent of businesses reported that they suffered a data breach caused by a vendor or third party[1]. This growing operational challenge also positions the IBM Z mainframe as a key platform within any hybrid cloud transition strategy. Cloud-specific security services, referred to as IBM Cloud Hyper Protect Services, provides:

◉ A complete set of encryption and key management services within a dedicated namespace.

◉ A database on-demand service that features the ability to store data in a fully encrypted database without needing specialized skills; and

◉ A secure Kubernetes cluster container service that enables a standardized, portable and scalable process for packaging applications.

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Cyber-resiliency best practices: Staying prepared for cyberattack

It seems that major headlines every week focus on data breaches or cyber events against well-known, reputable businesses or government agencies. Cyberattacks are becoming more prolific and sophisticated, so it’s no longer a question of if it will affect your organization, but when. Certain cyberattacks such as ransomware can cripple an organization, if not shut it down completely, which is why all organizations need to focus on cyber-resiliency.

Cyber-resiliency is the ability to continue operation in the event of a cyberattack. While there are multiple aspects of cyber-resiliency, in this post I want to focus on storage resiliency, which should be designed around three key assumptions:

1. Compromise is inevitable.
2. Critical data must be copied and stored beyond the reach of compromise.
3. Organizations must have the tools to automate, test and learn to recover when a breach or attack occurs.

Let’s break down each of these aspects and look at what organizations can do to bolster their cyber-resiliency.

Compromise is Inevitable

While it’s nearly impossible in today’s world to completely avoid data breaches or other cyberattacks, there are certain practices that enhance security and help protect against attacks:

◉ Discover and patch systems
◉ Automatically fix vulnerabilities
◉ Adopt a zero-trust policy

However, when an attack comes, you need a plan to be able to respond and recover rapidly.

Critical data must be copied and stored beyond the reach of compromise

Organizations need to understand what data is required for their operations to continue to run, such as customer account information and transactions. Protected copies of this mission-critical data shouldn’t be accessible and manipulatable on production systems, which can be compromised.

There are several important points of consideration in protecting data:

Limit privileged users: Often times, threats come from internal actors or an external agent that has compromised a super user, giving the attacker total control and the ability to corrupt and destroy production and backup data. You can help prevent this by limiting privileged accounts, and only authorizing access on as-needed basis.

Generate immutable copies: It’s critical to have protected copies of your data that can’t be manipulated. There are multiple storage possibilities for ensuring the immutability of your most critical data, such as Write Once Read Many (WORM) media like tape, cloud object storage or specialized storage devices. A snapshot that can be mounted to a host is still corruptible.

Maintain isolation: You also need to maintain a logical and physical separation between protected copies of the data and host systems. For example, put a network airgap between a host and its protected copies.

Consider performance: Different methods of data protection come with different performance characteristics, such as copy duration (How long will the backup take?) and performance implications to production, recovery point objective (RPO; How current is my protected data?) and recovery time objective (RTO; How fast I can restore my data?). Organizations will need to understand the tradeoffs between their budgets and their business objectives.

Organizations must have the tools to automate, test and learn to recover when a breach or attack occurs

Build automation: Restoration and recovery normally include multiple, complex steps and coordinating between multiple systems. The last thing you want to worry about in a high-pressure, time-critical situation is the possibility of user error. Automating recovery procedures will provide a consistent approach under any situation.

Make it easy to use: Recovery methods should be straightforward enough to be handled by operators and not require calling 10 different engineers, and that applies especially in a high-pressure situation. Tools, such as push-button web interfaces that can launch an automated disaster recovery process, make recovery more accessible.

Practice makes perfect: Testing the recovery process often is important, not only to validate the process but to provide familiarity to the ones executing it. This can be achieved using recovery systems that won’t affect production systems.

It’s not just important to focus on cybersecurity and the prevention of cyberattacks; it’s equally important to recover and continue operations from attacks, when they occur.

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