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How Mobile Apps Power Up On-Demand Startups

Granner Smith

On-demand is set to transform the mobile commerce entrepreneurial space. Whether you’re looking for taxi bookings, food orders, healthcare services, home maintenance, business info, or more, the app store has a solution for practically every service you can think of, and smartphone owners are more than willing to use these mobile apps. While innovative startups are already trying to take the market by storm, there are still countless opportunities available for people looking to make their mark. This means there’s a phenomenal growth outlook for e-commerce startups that provide unique services on-demand. On the other hand, there are equally big challenges to overcome, as the competition is daunting.

Understanding the basics of on-demand business

Many opportunities and challenges of on-demand service startups are similar to the those of conventional e-commerce businesses. The difference is how services are delivered – as the name suggests, on-demand businesses deliver services to the buyer when, how, and where they need them.

The unique selling proposition of on-demand (compared to traditional e-commerce) lies in its convenience and spontaneity. To be successful, on-demand startups are tasked with creating a unique business idea that has sustainability, scalability, and profitability over a period of time.

Before you venture into this space, it’s important to understand the on-demand service business model, which is based on the following components:

  1. Identify a pain point (demand): Identifying and solving a pain point is the basis of any business model. The more unique your idea, the better its chances of survival and success.
  1. Determine whether your service is instant or scheduled: Once you have a business idea, you have to work on how you’ll provide the service you are promising. One consideration is whether the service is instantly delivered or scheduled. For instance, food delivery is an instant service with the customer expecting a short wait time. Scheduled service could be an airline booking for a future point of time. Startups providing instant services must have adequate capacity and supply to meet excess demand as needed.
  1. Find a reliable staff supply: Meeting that latter point requires a steady and reliable source of staff and supplies. On the staffing side, startups may choose between contracted workers and freelancers. While contracted staff provide reliability, freelancers may be more cost-effective. Startups should try to strike an equilibrium. Begin with more stable contractual supply on a small scale and gradually add freelance support to scale to your growth.
  1. Strengthen the core: Once the operational side of the business is taken care of, you need to strengthen your core with the right technology, meaning the mobile app that links you with potential customers.
  1. Planning and patience: Finally, when you have all the processes in place, it’s time to streamline them. The integration between offline (operations) and online (app technology) is a complex task, an art that’s mastered with patience and precision. Be prepared to invest a good deal of effort and money to make your business a success. At the same time, be realistic in your expectations, as overnight success is unlikely.

On-demand startups must be prepared for slow-paced growth, but the results can be phenomenal if they can sustain themselves through the testing phase. Creating a sound business strategy and adhering to it is the best way to proceed.

Mobile app: the lifeline of on-demand business

The entire concept of on-demand business is woven around mobility. Its services must be available anywhere and anytime, making the mobile app an essential ingredient of the business. An app is the platform by which the business accesses the market, provides services to users, and retains loyal customers. Here are the mobile app features needed to give users a great experience and bring business to the startup:

  • Convenience: On-demand service is synonymous with convenience. Convenience is not confined to delivering the service, but encompasses the entire performance of the app. The app should load quickly and have an excellent user interface. The entire checkout process should be quick and smooth, completed with a minimum number of clicks, and have few forms. Simplicity can be a deciding factor in engaging users, converting them, and bringing them back.
  • Live tracking: Real-time tracking that sends location-based offers to customers and enables them to track their order or service ensures customer satisfaction and helps build long-term loyalty.
  • Seamless payment: Customers prefer mobile apps that enable cashless transactions through the most popular, secure, and seamless payment options.
  • Reviews and ratings: Customer ratings are a key element in an on-demand business’ growth, as potential customers are more likely to have confidence in reviews and ratings provided by actual users. Real-time customer feedback is also an effective way for a business to continuously evaluate its performance.

A mobile app is a lifeline for an on-demand startup. It matches supply with demand to enable the business to deliver the service at the right place and the right time. For startups providing services on demand, the main driving force in growth is not money or inventory, but technology in the form of mobile apps.

For more on digital selling, see Primed: Prompting Customers to Buy.

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About Granner Smith

Granner Smith is a Professional writer. His skill set is vast, his greatest expertise revolve in the worlds of interactive design, development, UX, social media, brand identity design, content creation. He works with reputed company, Orange Mantra that provide web and mobility solution. Follow us on Twitter @Orangemantraggn Facebook @OrangeMantraindia

It's A Phygital World

KC Krishnadas

In May 2016, nearly 150 years after Jamsetji Nusserwanji Tata started a private trading firm in 1868 that grew into the giant Tata Group, TataCLiQ was launched. It was the conglomerate’s first venture into pure-play digital. This e-commerce venture, however, is different from the likes of Amazon or Flipkart in that it converges physical with digital retail, delivering India’s first true “omnichannel” customer experience. Its executives call it “phygital,” a platform that allows shoppers to order, collect, return, or exchange products either online or at any of its brand partners’ brick-and-mortar stores.

I have a fairly prosaic definition of digital because it helps me focus on where I think the disruptions can happen. While enterprises have seen the first wave of disruption due to ‘digital-only platforms,’ the second wave of digital will be ‘phygital’—a combination of physical and digital, commonly called ‘omnichannel.’ –KRS Jamwal, executive director, Tata Industries and Digitalist 2017

The ‘Q’ in the TataCLiQ logo is in the form of a magnifying glass—a visual representation of the brand’s focus on only the best, most authentic brands and products. Unlike other Indian e-tailers that have burnt big-time cash in their quest for growth, this one-year-old enterprise is already disrupting the disruptors through tech-enabled innovation.

When Tricia Manning-Smith, senior on-air global correspondent, Customer Storytelling Team, SAP, dropped in to meet senior TataCLiQ executives Ashutosh Pandey, CEO; Vikas Purohit, COO; and Sauvik Banerjjee, CTO—as well as K.R.S. Jamwal, executive director, Tata Industries Ltd.—to find out more about TataCLiQ, Digitalist magazine decided to accompany her. Edited excerpts from the conversation with the Tata executives follow:

Manning-Smith: What makes TataCLiQ unique?

Banerjjee: It has enabled the brick-and-mortar model online. When you place an order, the store fulfilling it will be one close to your address, or to the pin code of the location you reside in. Ours is not a warehouse model, but a store based fulfilment one. If you place an order for a blue shirt of a certain brand, for instance, you are informed of the stores nearest to your home that stock it. You can either collect it from these stores or have it delivered home, whichever you prefer.

TataCLiQ also opens up all luxury brands. Retailers across product categories love that. Who wouldn’t want his inventory problem in the franchise-driven retail world solved? People thought we were entering the game late. What they did not understand is that we have the loyalty of a huge pan-India customer base.

The consumer shops online and picks up selected products from a nearby store or has it delivered home. Every part of India has a pin code, so when an order is placed, the store nearest to your home puts up its hand and competes with other stores nearby to fulfill your order. The seller can continuously see the orders and where they are coming from. It is similar to the lodging or the taxi businesses now around the world that own neither hotels nor taxis.

Can you buy anything on TataCLiQ?

Banerjjee: It now has clothing for men and women, electronics, footwear, watches, and accessories, but more categories will be added. Many international brands are also seeking to enter the Indian market through Tata-CLiQ .

Can you elaborate some more on the ‘phygital’ experience?

Pandey: Retail, enabling customers to pick up products from a store or having it shipped to their homes is uni-dimensional, easy to do. In India, many brands do not reach beyond the top 10-12 cities because of infrastructure issues. What we have done, for the first time in the world, is become an omnichannel provider for many big brands.

We can scale brand by brand. It’s better than putting up the physical infrastructure ourselves, which would be duplication. This model removes the physical-digital divide by giving customers a seamless experience across both worlds. Conventionally, customers have one or the other, not both and not seamlessly. So ‘phygital’ is combining the physical and digital seamlessly. We were not the first to go to market. Competition existed, and so we had to bring in a degree of innovation and value.

Does TataCLiQ understand India better than its e-tailing rivals?

Pandey: The Tata group has businesses from salt to fertilizers to automobiles to software, so it understands many aspects of India better. In retail, we have the combined learning from Titan, Westside, and Tanishq, where we touch millions of customers every day. This learning is built over a period of time. While algorithms and data can tell you some things, handling so many customers over the years gives you knowledge and insights like nothing else can.

“Every customer experience is important. The customer is not a statistic. He or she is a living person. The day we have the mastery of trying to make her happy through our offering, through our service, through our end-to-end experience that we provide to her, that’s the time you know multiple things can happen.” –Ashutosh Pandey, Chief Executive Officer, TataCLiQ.com

Banerjjee: We personalize the platform on a daily basis using algorithms. We track web behavior, anonymous user behavior, IP address behavior, where orders are coming from, purchasing history, the demographics of buying for specific products, brands, and the like. Our customer service teams work round the clock, answering queries on orders, product availability, or delivery issues—everything from placing an order to having it delivered. We monitor social media feedback and continuously respond to it, usually within 5-10 minutes. If there is any kind of noise created in social media, we try and ensure that at the end we have a happy customer.

How does the logistics work for a country as big as India?

Banerjjee: What we aim at is a threefold model: traditional, online fulfillment using the large 3PLs, and hyperlocal companies. City-based fulfillment providers do the delivery. They are far more agile than big logistics providers within a city.

How does a live business like TataCLiQ help Indians get what they want, when they want it?

Purohit: Our purpose of existence is to connect buyers from [remote parts of the country] to well-known brands, doing so with the confidence that the product is authentic, delivery is swift, and [the customer has] a great buying experience. What will stand out when doing this is that we take every single customer issue seriously, to the point that we have designed a bespoke metric—a customer obsessiveness index. This index must go up as close to 100 as possible. We are not yet there, but we are on our way.

Jamwal: The Tata founders were visionary when they said that the society is not just another stakeholder but the very reason for our existence. There are few business houses or industrialists who look at society as the reason for existence. The group’s history has been one of organic but structured growth. The plan was not to build a conglomerate but to impact lives, build a nation. This was the ethos of the founded and has been carried on over the decades. That won’t go away.

“India is championing a proper marriage between online and brick and mortar, and that’s where the proposition of TataCLiQ. Its an online store inventory reconciliation with visibility to the consumer from a product attribute, SKU by SKU basis.” –Sauvik Banerjjee, chief technology officer, TataCLiQ .com

Banerjjee: We came here in a Tata vehicle; Jaguar Land Rover is a Tata company. Many trucks on the road we traveled were Tata trucks. Some hoardings on the way were of our consumer-facing brands—Titan, Tanishq, Westside, and Croma. We have Tata Salt, a hugely popular brand of table salt, Tata Beverages with its mineral water and fizzy drinks. It will take all day to list our brands, the businesses we are in.

India is home to 1.4 billion people, and we cannot imagine a single adult in the country who is not consciously or otherwise touching a Tata product every day. We see it as a humongous responsibility, for with great power that comes as $100 billion-dollar business comes great responsibility.

How important is digitalization to the Tata group?

Banerjjee: Let’s put it this way: India is expected to be the third-largest digital economy by 2020, according to data from preeminent research organisations. The digital economy, whether in fintech, IoT, omnichannel, or digital banking, will be massive. With India set to become the third-largest digital economy in the next three years, the Tata group will play a big role in enabling and augmenting it, giving it velocity.

How important is trust in the digital world?

Banerjjee: Digitalization, by its very nature, brings privacy almost to an end. You do not know what the data is being used for. People sometimes may not trust sites that sell all kinds of things. That is why cash on delivery is quite widespread in this country. That is where known brands like the Tata brand, which people trust to do the right things for them as it has done over the decades, count. In our hyper-connected world, people need anchors and trust is one of the biggest anchors. That is the reason for the continued relevance of the Tata group.

“We have realized that most marketplaces have become catalog aggregators vis-a-vis becoming a catalogue authority. That’s the difference in approach that a marketplace can take.” –Vikas Purohit, chief operating officer, TataCLiQ.com

What is TataCLiQ doing to make customers feel special?

Banerjjee: Growth alone is not enough. We are clear that unless the customer experience is at a level where customer falls in love with you, there is no point trying to scale. Every customer experience is important. The customer is not a statistic, but a living person. As broadband percolates across the country, more smartphone users are enabled every passing day and personalization becomes a bigger challenge. That is what we are trying to crack, and it is a great challenge to have.

The day we master the art of making the customer happy through our offerings, our service, our end-to-end experience, multiple things can happen. At the end of the day, we are all customers and we would all like to feel special. The place we are shopping at must understand us as people. That is our goal.

For more insight on where customer engagement is headed, see Customer Experiences Must Be OmniChannel, OmniNow, And OmniWow.

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About KC Krishnadas

KC Krishnadas is the editor of FactorBranded, a brand solutions media agency. He has 25 years of experience covering technology at The Economic Times and later as the India correspondent for EE Times.

Digitalist Flash Briefing: Using Social Media to Streamline Customer Interactions

Peter Johnson

Today, we’re looking at another company that uses digital tools to meet their audience online.

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Peter Johnson

About Peter Johnson

Peter Johnson is a Senior Director of Marketing Strategy and Thought Leadership at SAP, responsible for developing easy to understand corporate level and cross solution messaging. Peter has proven experience leading innovative programs to accelerate and scale Go-To-Market activities, and drive operational efficiencies at industry leading solution providers and global manufactures respectively.

Data Lakes: Deep Insights

Timo Elliott, John Schitka, Michael Eacrett, and Carolyn Marsan

Dan McCaffrey has an ambitious goal: solving the world’s looming food shortage.

As vice president of data and analytics at The Climate Corporation (Climate), which is a subsidiary of Monsanto, McCaffrey leads a team of data scientists and engineers who are building an information platform that collects massive amounts of agricultural data and applies machine-learning techniques to discover new patterns. These analyses are then used to help farmers optimize their planting.

“By 2050, the world is going to have too many people at the current rate of growth. And with shrinking amounts of farmland, we must find more efficient ways to feed them. So science is needed to help solve these things,” McCaffrey explains. “That’s what excites me.”

“The deeper we can go into providing recommendations on farming practices, the more value we can offer the farmer,” McCaffrey adds.

But to deliver that insight, Climate needs data—and lots of it. That means using remote sensing and other techniques to map every field in the United States and then combining that information with climate data, soil observations, and weather data. Climate’s analysts can then produce a massive data store that they can query for insights.

Meanwhile, precision tractors stream data into Climate’s digital agriculture platform, which farmers can then access from iPads through easy data flow and visualizations. They gain insights that help them optimize their seeding rates, soil health, and fertility applications. The overall goal is to increase crop yields, which in turn boosts a farmer’s margins.

Climate is at the forefront of a push toward deriving valuable business insight from Big Data that isn’t just big, but vast. Companies of all types—from agriculture through transportation and financial services to retail—are tapping into massive repositories of data known as data lakes. They hope to discover correlations that they can exploit to expand product offerings, enhance efficiency, drive profitability, and discover new business models they never knew existed.

The internet democratized access to data and information for billions of people around the world. Ironically, however, access to data within businesses has traditionally been limited to a chosen few—until now. Today’s advances in memory, storage, and data tools make it possible for companies both large and small to cost effectively gather and retain a huge amount of data, both structured (such as data in fields in a spreadsheet or database) and unstructured (such as e-mails or social media posts). They can then allow anyone in the business to access this massive data lake and rapidly gather insights.

It’s not that companies couldn’t do this before; they just couldn’t do it cost effectively and without a lengthy development effort by the IT department. With today’s massive data stores, line-of-business executives can generate queries themselves and quickly churn out results—and they are increasingly doing so in real time. Data lakes have democratized both the access to data and its role in business strategy.

Indeed, data lakes move data from being a tactical tool for implementing a business strategy to being a foundation for developing that strategy through a scientific-style model of experimental thinking, queries, and correlations. In the past, companies’ curiosity was limited by the expense of storing data for the long term. Now companies can keep data for as long as it’s needed. And that means companies can continue to ask important questions as they arise, enabling them to future-proof their strategies.

Prescriptive Farming

Climate’s McCaffrey has many questions to answer on behalf of farmers. Climate provides several types of analytics to farmers including descriptive services, which are metrics about the farm and its operations, and predictive services related to weather and soil fertility. But eventually the company hopes to provide prescriptive services, helping farmers address all the many decisions they make each year to achieve the best outcome at the end of the season. Data lakes will provide the answers that enable Climate to follow through on its strategy.

Behind the scenes at Climate is a deep-science data lake that provides insights, such as predicting the fertility of a plot of land by combining many data sets to create accurate models. These models allow Climate to give farmers customized recommendations based on how their farm is performing.

“Machine learning really starts to work when you have the breadth of data sets from tillage to soil to weather, planting, harvest, and pesticide spray,” McCaffrey says. “The more data sets we can bring in, the better machine learning works.”

The deep-science infrastructure already has terabytes of data but is poised for significant growth as it handles a flood of measurements from field-based sensors.

“That’s really scaling up now, and that’s what’s also giving us an advantage in our ability to really personalize our advice to farmers at a deeper level because of the information we’re getting from sensor data,” McCaffrey says. “As we roll that out, our scale is going to increase by several magnitudes.”

Also on the horizon is more real-time data analytics. Currently, Climate receives real-time data from its application that streams data from the tractor’s cab, but most of its analytics applications are run nightly or even seasonally.

In August 2016, Climate expanded its platform to third-party developers so other innovators can also contribute data, such as drone-captured data or imagery, to the deep-science lake.

“That helps us in a lot of ways, in that we can get more data to help the grower,” McCaffrey says. “It’s the machine learning that allows us to find the insights in all of the data. Machine learning allows us to take mathematical shortcuts as long as you’ve got enough data and enough breadth of data.”

Predictive Maintenance

Growth is essential for U.S. railroads, which reinvest a significant portion of their revenues in maintenance and improvements to their track systems, locomotives, rail cars, terminals, and technology. With an eye on growing its business while also keeping its costs down, CSX, a transportation company based in Jacksonville, Florida, is adopting a strategy to make its freight trains more reliable.

In the past, CSX maintained its fleet of locomotives through regularly scheduled maintenance activities, which prevent failures in most locomotives as they transport freight from shipper to receiver. To achieve even higher reliability, CSX is tapping into a data lake to power predictive analytics applications that will improve maintenance activities and prevent more failures from occurring.

Beyond improving customer satisfaction and raising revenue, CSX’s new strategy also has major cost implications. Trains are expensive assets, and it’s critical for railroads to drive up utilization, limit unplanned downtime, and prevent catastrophic failures to keep the costs of those assets down.

That’s why CSX is putting all the data related to the performance and maintenance of its locomotives into a massive data store.

“We are then applying predictive analytics—or, more specifically, machine-learning algorithms—on top of that information that we are collecting to look for failure signatures that can be used to predict failures and prescribe maintenance activities,” says Michael Hendrix, technical director for analytics at CSX. “We’re really looking to better manage our fleet and the maintenance activities that go into that so we can run a more efficient network and utilize our assets more effectively.”

“In the past we would have to buy a special storage device to store large quantities of data, and we’d have to determine cost benefits to see if it was worth it,” says Donna Crutchfield, assistant vice president of information architecture and strategy at CSX. “So we were either letting the data die naturally, or we were only storing the data that was determined to be the most important at the time. But today, with the new technologies like data lakes, we’re able to store and utilize more of this data.”

CSX can now combine many different data types, such as sensor data from across the rail network and other systems that measure movement of its cars, and it can look for correlations across information that wasn’t previously analyzed together.

One of the larger data sets that CSX is capturing comprises the findings of its “wheel health detectors” across the network. These devices capture different signals about the bearings in the wheels, as well as the health of the wheels in terms of impact, sound, and heat.

“That volume of data is pretty significant, and what we would typically do is just look for signals that told us whether the wheel was bad and if we needed to set the car aside for repair. We would only keep the raw data for 10 days because of the volume and then purge everything but the alerts,” Hendrix says.

With its data lake, CSX can keep the wheel data for as long as it likes. “Now we’re starting to capture that data on a daily basis so we can start applying more machine-learning algorithms and predictive models across a larger history,” Hendrix says. “By having the full data set, we can better look for trends and patterns that will tell us if something is going to fail.”

Another key ingredient in CSX’s data set is locomotive oil. By analyzing oil samples, CSX is developing better predictions of locomotive failure. “We’ve been able to determine when a locomotive would fail and predict it far enough in advance so we could send it down for maintenance and prevent it from failing while in use,” Crutchfield says.

“Between the locomotives, the tracks, and the freight cars, we will be looking at various ways to predict those failures and prevent them so we can improve our asset allocation. Then we won’t need as many assets,” she explains. “It’s like an airport. If a plane has a failure and it’s due to connect at another airport, all the passengers have to be reassigned. A failure affects the system like dominoes. It’s a similar case with a railroad. Any failure along the road affects our operations. Fewer failures mean more asset utilization. The more optimized the network is, the better we can service the customer.”

Detecting Fraud Through Correlations

Traditionally, business strategy has been a very conscious practice, presumed to emanate mainly from the minds of experienced executives, daring entrepreneurs, or high-priced consultants. But data lakes take strategy out of that rarefied realm and put it in the environment where just about everything in business seems to be going these days: math—specifically, the correlations that emerge from applying a mathematical algorithm to huge masses of data.

The Financial Industry Regulatory Authority (FINRA), a nonprofit group that regulates broker behavior in the United States, used to rely on the experience of its employees to come up with strategies for combating fraud and insider trading. It still does that, but now FINRA has added a data lake to find patterns that a human might never see.

Overall, FINRA processes over five petabytes of transaction data from multiple sources every day. By switching from traditional database and storage technology to a data lake, FINRA was able to set up a self-service process that allows analysts to query data themselves without involving the IT department; search times dropped from several hours to 90 seconds.

While traditional databases were good at defining relationships with data, such as tracking all the transactions from a particular customer, the new data lake configurations help users identify relationships that they didn’t know existed.

Leveraging its data lake, FINRA creates an environment for curiosity, empowering its data experts to search for suspicious patterns of fraud, marketing manipulation, and compliance. As a result, FINRA was able to hand out 373 fines totaling US$134.4 million in 2016, a new record for the agency, according to Law360.

Data Lakes Don’t End Complexity for IT

Though data lakes make access to data and analysis easier for the business, they don’t necessarily make the CIO’s life a bed of roses. Implementations can be complex, and companies rarely want to walk away from investments they’ve already made in data analysis technologies, such as data warehouses.

“There have been so many millions of dollars going to data warehousing over the last two decades. The idea that you’re just going to move it all into a data lake isn’t going to happen,” says Mike Ferguson, managing director of Intelligent Business Strategies, a UK analyst firm. “It’s just not compelling enough of a business case.” But Ferguson does see data lake efficiencies freeing up the capacity of data warehouses to enable more query, reporting, and analysis.

Data lakes also don’t free companies from the need to clean up and manage data as part of the process required to gain these useful insights. “The data comes in very raw, and it needs to be treated,” says James Curtis, senior analyst for data platforms and analytics at 451 Research. “It has to be prepped and cleaned and ready.”

Companies must have strong data governance processes, as well. Customers are increasingly concerned about privacy, and rules for data usage and compliance have become stricter in some areas of the globe, such as the European Union.

Companies must create data usage policies, then, that clearly define who can access, distribute, change, delete, or otherwise manipulate all that data. Companies must also make sure that the data they collect comes from a legitimate source.

Many companies are responding by hiring chief data officers (CDOs) to ensure that as more employees gain access to data, they use it effectively and responsibly. Indeed, research company Gartner predicts that 90% of large companies will have a CDO by 2019.

Data lakes can be configured in a variety of ways: centralized or distributed, with storage on premise or in the cloud or both. Some companies have more than one data lake implementation.

“A lot of my clients try their best to go centralized for obvious reasons. It’s much simpler to manage and to gather your data in one place,” says Ferguson. “But they’re often plagued somewhere down the line with much more added complexity and realize that in many cases the data lake has to be distributed to manage data across multiple data stores.”

Meanwhile, the massive capacities of data lakes mean that data that once flowed through a manageable spigot is now blasting at companies through a fire hose.

“We’re now dealing with data coming out at extreme velocity or in very large volumes,” Ferguson says. “The idea that people can manually keep pace with the number of data sources that are coming into the enterprise—it’s just not realistic any more. We have to find ways to take complexity away, and that tends to mean that we should automate. The expectation is that the information management software, like an information catalog for example, can help a company accelerate the onboarding of data and automatically classify it, profile it, organize it, and make it easy to find.”

Beyond the technical issues, IT and the business must also make important decisions about how data lakes will be managed and who will own the data, among other things (see How to Avoid Drowning in the Lake).

How to Avoid Drowning in the Lake

The benefits of data lakes can be squandered if you don’t manage the implementation and data ownership carefully.

Deploying and managing a massive data store is a big challenge. Here’s how to address some of the most common issues that companies face:

Determine the ROI. Developing a data lake is not a trivial undertaking. You need a good business case, and you need a measurable ROI. Most importantly, you need initial questions that can be answered by the data, which will prove its value.

Find data owners. As devices with sensors proliferate across the organization, the issue of data ownership becomes more important.

Have a plan for data retention. Companies used to have to cull data because it was too expensive to store. Now companies can become data hoarders. How long do you store it? Do you keep it forever?

Manage descriptive data. Software that allows you to tag all the data in one or multiple data lakes and keep it up-to-date is not mature yet. We still need tools to bring the metadata together to support self-service and to automate metadata to speed up the preparation, integration, and analysis of data.

Develop data curation skills. There is a huge skills gap for data repository development. But many people will jump at the chance to learn these new skills if companies are willing to pay for training and certification.

Be agile enough to take advantage of the findings. It used to be that you put in a request to the IT department for data and had to wait six months for an answer. Now, you get the answer immediately. Companies must be agile to take advantage of the insights.

Secure the data. Besides the perennial issues of hacking and breaches, a lot of data lakes software is open source and less secure than typical enterprise-class software.

Measure the quality of data. Different users can work with varying levels of quality in their data. For example, data scientists working with a huge number of data points might not need completely accurate data, because they can use machine learning to cluster data or discard outlying data as needed. However, a financial analyst might need the data to be completely correct.

Avoid creating new silos. Data lakes should work with existing data architectures, such as data warehouses and data marts.

From Data Queries to New Business Models

The ability of data lakes to uncover previously hidden data correlations can massively impact any part of the business. For example, in the past, a large soft drink maker used to stock its vending machines based on local bottlers’ and delivery people’s experience and gut instincts. Today, using vast amounts of data collected from sensors in the vending machines, the company can essentially treat each machine like a retail store, optimizing the drink selection by time of day, location, and other factors. Doing this kind of predictive analysis was possible before data lakes came along, but it wasn’t practical or economical at the individual machine level because the amount of data required for accurate predictions was simply too large.

The next step is for companies to use the insights gathered from their massive data stores not just to become more efficient and profitable in their existing lines of business but also to actually change their business models.

For example, product companies could shield themselves from the harsh light of comparison shopping by offering the use of their products as a service, with sensors on those products sending the company a constant stream of data about when they need to be repaired or replaced. Customers are spared the hassle of dealing with worn-out products, and companies are protected from competition as long as customers receive the features, price, and the level of service they expect. Further, companies can continuously gather and analyze data about customers’ usage patterns and equipment performance to find ways to lower costs and develop new services.

Data for All

Given the tremendous amount of hype that has surrounded Big Data for years now, it’s tempting to dismiss data lakes as a small step forward in an already familiar technology realm. But it’s not the technology that matters as much as what it enables organizations to do. By making data available to anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.

“Companies that do not actively invest in data lakes will truly be left behind,” says Anita Raj, principal growth hacker at DataRPM, which sells predictive maintenance applications to manufacturers that want to take advantage of these massive data stores. “So it’s just the option of disrupt or be disrupted.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


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Timo Elliott is Vice President, Global Innovation Evangelist, at SAP.

John Schitka is Senior Director, Solution Marketing, Big Data Analytics, at SAP.

Michael Eacrett is Vice President, Product Management, Big Data, Enterprise Information Management, and SAP Vora, at SAP.

Carolyn Marsan is a freelance writer who focuses on business and technology topics.

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About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in articles such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

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Artificial Intelligence: The Future Of Oil And Gas

Anoop Srivastava

Oil prices have fallen dramatically over last few years, forcing some major oil companies to take drastic actions such as layoffs, cutting investments and budgets, and more. Shell, for example, shelved its plan to invest in Qatar, Aramco put on hold its deep-water exploration in the Red Sea, Schlumberger fired a few thousand employees, and the list goes on…

In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak. Operations in the oil and gas industry today means balancing a dizzying array of trade-offs in the drive for competitive advantage while maximizing return on investment.

The result is a dire need to optimize performance and optimize the cost of production per barrel. Companies have many optimization opportunities once they start using the massive data being generated by oil fields. Oil and gas companies can turn this crisis into an opportunity by leveraging technological innovations like artificial intelligence to build a foundation for long-term success. If volatility in oil prices is the new norm, the push for “value over volume” is the key to success going forward.

Using AI tools, upstream oil and gas companies can shift their approach from production at all costs to producing in context. They will need to do profit and loss management at the well level to optimize the production cost per barrel. To do this, they must integrate all aspects of production management, collect the data for analysis and forecasting, and leverage artificial intelligence to optimize operations.

When remote sensors are connected to wireless networks, data can be collected and centrally analyzed from any location. According to the consulting firm McKinsey, the oil and gas supply chain stands to gain $50 billion in savings and increased profit by adopting AI. As an example, using AI algorithms to more accurately sift through signals and noise in seismic data can decrease dry wellhead development by 10 percent.

How oil and gas can leverage artificial intelligence

1. Planning and forecasting

On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.

2. Eliminate costly risks in drilling

Drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well cleaning predictions. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making.

When drilling, machine-learning software takes into consideration a plethora of factors, such as seismic vibrations, thermal gradients, and strata permeability, along with more traditional data such as pressure differentials. AI can help optimize drilling operations by driving decisions such as direction and speed in real time, and it can predict failure of equipment such as semi-submersible pumps (ESPs) to reduce unplanned downtime and equipment costs.

3. Well reservoir facility management

Wells, reservoirs, and facility management includes integration of multiple disciplines: reservoir engineering, geology, production technology, petro physics, operations, and seismic interpretation. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance.

AI techniques can also be applied in other activities such as reservoir characterization, modeling and     field surveillance. Fuzzy logic, artificial neural networks and expert systems are used extensively across the industry to accurately characterize reservoirs in order to attain optimum production level.

Today, AI systems form the backbone of digital oil field (DOF) concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.

4. Predictive maintenance

Today, artificial intelligence is taking the industry by storm. AI-powered software and sensor hardware enables us to use very large amounts of data to gain real-time responses on the best future course of action. With predictive analytics and cognitive security, for example, oil and gas companies can operate equipment safely and securely while receiving recommendations on how to avoid future equipment failure or mediate potential security breaches.

5. Oil and gas well surveying and inspections

Drones have been part of the oil and gas industry since 2013, when ConocoPhillips used the Boeing ScanEagle drone in trials in the Chukchi Sea.  In June 2014, the Federal Aviation Administration (FAA) issued the first commercial permit for drone use over United States soil to BP, allowing the company to survey pipelines, roads, and equipment in Prudhoe Bay, Alaska. In January, Sky-Futures completed the first drone inspection in the Gulf of Mexico.

While drones are primarily used in the midstream sector, they can be applied to almost every aspect of the industry, including land surveying and mapping, well and pipeline inspections, and security. Technology is being developed to enable drones to detect early methane leaks. In addition, one day, drones could be used to find oil and gas reservoirs underlying remote uninhabited regions, from the comfort of a warm office.

6. Remote logistics

As logistics to offshore locations is always a challenge, AI-enhanced drones can be used to deliver materials to remote offshore locations.

Current adoption of AI

Chevron is currently using AI to identify new well locations and simulation candidates in California. By using AI software to analyze the company’s large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods. Chevron is also using predictive models to analyze the performance of thousands of pieces of rotating equipment to detect failures before they occur. By addressing problems before they become critical, Chevron has avoided unplanned shutdowns and lowered repair expenses. Increased production and lower costs have translated to more profit per well.

Future journey

Today’s oil and gas industry has been transformed by two industry downturns in one decade. Although adoption of new hard technology such as directional drilling and hydraulic fracturing (fracking) has helped, the oil and gas industry needs to continue to innovate in today’s low-price market to survive. AI has the potential to differentiate companies that thrive and those that are left behind.

The promise of AI is already being realized in the oil and gas industry. Early adopters are taking advantage of their position  to get a head start on the competition and protect their assets. The industry has always leveraged technology to adapt to change, and early adopters have always benefited the most. As competition in the oil and gas industry continues to heat up, companies cannot afford to be left behind. For those that understand and seize the opportunities inherent in adopting cognitive technologies, the future looks bright.

For more insight on advanced technology in the energy sector, see How Digital Transformation Is Refueling The Energy Industry.

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Anoop Srivastava

About Anoop Srivastava

Anoop Srivastava is Senior Director of the Energy and Natural Resources Industries at SAP Value Engineering in Middle East and North Africa. He advises clients on their digital transformation strategies and helps them align their business strategy with IT strategy leveraging digital technology innovations such as the Internet of Things, Big Data, Advanced Analytics, Cloud etc. He has 21+ years of work experience spanning across Oil& Gas Industry, Business Consulting, Industry Value Advisory and Digital Transformation.