How is Telecom Using Cloud Computing


The Telecom sector has been witnessing major shifts in recent time – rising traffic volumes, falling incomes, growing difficulties in managing facility investment, and so on. It is in this context that many Telecom companies have started to explore the possibilities of Cloud Computing. How is the Telecom industry using Cloud Computing, though? Here’s how.

Optimize business processes:

Cloud-based services used by the telecom industry are distinct from those used by the IT sector – the telecom industry’s requirements for high levels of availability and reliability must be met. Additionally, telecom operators also seek to utilize the significant investment they have already made in their existing infrastructure. Telecom software typically runs on dedicated computer systems. Launching new network services implies that service providers are faced with the complexity of managing multiple proprietary pieces of hardware and integrating these physical devices in a network. The Telecom Cloud can help address these issues effectively.

Making use of Local Presence to provide Cloud Services:

A Telecom Cloud provider is a telecommunications company that has moved a significant part of its resources from providing landline service to providing Cloud Computing services.

In the early days of Cloud Computing, when the cry was for services that were globally homogeneous, the local presence of telecom operators was seen as a liability. But today, telecom operators are actually turning their local presence into a key factor in their Cloud business strategy. For instance, they are working in collaboration with local software traders and using their SME and LME customer base to expand their services in each market.

Telecom operators can also make use of their local presence to identify local SMEs that had not yet made heavy investments in IT, and offer them customized Cloud applications. However, these applications should not be mere copies of generic applications suited to the business conditions of large enterprises. Rather, they should be designed taking into account the peculiar characteristics and needs of local enterprises.

Telecom companies should also keep a close watch on the trends current in enterprise and IT. For instance, a number of industry verticals – healthcare, education, retail, and logistics – may be looking to downsize their IT departments, which makes them likely prospects as customers for Cloud Computing services.

Future Prospects for profitable business:

Today, Cloud business is more than just connecting IT resources and services. Machine-to-machine (M2M) communications are increasingly gaining foreground in services focused on vertical industries (healthcare, education, retail, and government). In response to the needs of vertical industries, increasing amounts of data are being accumulated from outside the cloud. This mandates the integration of services and processes through a wider range of sensors – to be linked with the telecom operator’s Cloud. This provides a prime opportunity for telecom operators to expand Cloud-based business.

The shift to Cloud services allows telecom companies to repurpose underutilised networking resources (relieved mainly from numerous dropped landline connections) and make effective use of existing business relationships. It is likely that in the near future we will see the telecom sector leveraging Cloud technology in even bigger ways.

Why Your Organisation Needs Big Data


Consumer product companies are using it to predict customer behaviour, preferences, and product perception. Advertising and marketing agencies are using it to understand responsiveness to campaigns, promotions, and other advertising mediums. Manufacturers are using to it to predict the optimal time for replacement or maintenance of their products and to trace after-market support issues. Insurance companies are using it to see distinguish between the home insurance applications that can be immediately processed, and the ones that need a validating visit from an agent.

The ‘it’ here is Big Data. What is Big Data, though? Simply put, Big Data is the mining and processing of petabytes of information to gain insights into consumer behavior, promotion success, distribution efficiency, and a multitude of other aspects of business performance. An important question that may arise is whether such advanced analytics capability is worth the investment. In other words, does your organisation really need Big Data? Indeed, it does. Here’s why:

Understanding Target Audience

Big Data analysis allows Wal-Mart to better predict what products will sell and car insurance companies to understand how safely their customers actually drive. Even politician are making heavy use of Big Data and analytics to optimize their election campaigns – be it Obama in the US in 2012 or Narendra Modi in India in 2014.

Understanding the target audience and their preferences and buying patterns is one of the major areas where Big Data is being leveraged in a big way today. Companies are expanding their field of analysis by combining their traditional data sets with social media data, browser logs, text analytics, and sensor data to get a fuller picture of their customers. The most obvious and important objective is, of course, to create predictive models for successful business performance. Therefore, if you want to better the chances of your products and services succeeding with your clientele, your organisation needs a Big Data plan.

Smarter Strategizing:

The Harvard Business Review reports that the New York City Police Department makes use of Big Data Analysis “to geolocate and analyze ‘historical arrest patterns’ while cross-tab­bing them with sporting events, paydays, rainfall, traffic flows, and federal holidays to identify what NYPD calls likely crime ‘hot spots.’” Apparently, “such insight can help deploy officers to locations where crimes are likely to occur before they are actually committed.”

Indeed, the days of going with your gut alone are long gone – even for the police force, arguably one of the most instinct-driven of organizations. Today, technology makes it possible for the NYPD and other police departments to accurately anticipate and identify crime for swifter action. The same logic can be applied in a number of other fields – and Big Data is today being lever­aged in many indus­tries with impressive results. Financial institutions are using data mined from customer interactions to categorise their users into finely tuned segments that enable them to create increasingly relevant offers, web-based businesses are developing information products that combine data gathered from customers to offer more appealing recommendations and coupon programs, sports teams are using data for tracking team strategies and ticket sales, and so on.

This does not suggest that the role of human intuition, emotion, and reason in business have all been rendered redundant. But it does imply that Big Data is creating a context in which organisations can operate more efficiently. A well crafted and meticulously executed Big Data and analytics strategy makes your organization smarter.

Optimizing business processes:

Based on predictions generated from social media data, web search trends, and weather forecasts, retailers are now able to optimise their stock. HR businesses are increasingly optimizing their talent acquisition methods, as well as measurement of company culture and staff engagement using big data tools. Big Data is also being effectively tapped for supply chain optimization. Here, geographic positioning and radio frequency identification sensors are used to track goods or delivery vehicles and optimise routes by integrating live traffic data, driving conditions, etc.

Thus, Big Data is being increasingly used to optimise business processes – for organisations big and small. It can similarly help your organisation function at an optimal level.

Enabling cross-channel conversations with customers:

For most business orga­ni­za­tions today, it is all but impossible to maintain the kind of quality conversation with customers that laid the foundation for consumer trust in the yesteryears. The volume of queries, comments, and feedback coming in from various sources is simply too massive. This is where tech­ni­cal infra­struc­ture to sup­port dynamic, cross-channel con­ver­sa­tions with customers becomes absolutely nec­es­sary for orga­ni­sa­tional impact. Investing in a sound Big Data plan lets you stay close to your customers by providing you with customer insight.

McKinsey&Company’s iConsumer research suggests that “behavioral and transactional data from web usage, in-person observational research, and interviews can be analyzed to help companies make the right investments, whether based upon global, market, and customer segment level trends, or discreet consumer usage, buying factors, and attitudes.”

Preparing your orga­ni­za­tion for reversals:

The iConsumer report accounts for why organisations need to make structural changes related to Big Data. Reversals, as has been interestingly noted, “come gradually until they come suddenly.” For instance, the news­pa­per indus­try moved from boom­ing to near obso­lete within a decade of the advent of online pub­lish­ing. Another rever­sal was seen in the recording indus­try, which saw CD sales go from booming to obsolete with the advent of dig­i­tal music – again within a decade. Both these reversals were initially grad­ual and non-alarming, but they suddenly became massive in their impact.

Industry analysts and media experts anticipate the dig­i­ti­za­tion of all customer-facing orga­ni­za­tional sys­tems (ser­vice, sales,marketing) as the next major reversal. Thus, from the smallest local chains to the largest multinational companies – organisations that resist a systematic approach to data analysis, online marketing, digital distribution, etc. run the risk of becoming obsolete when the reversal comes suddenly. Adopting Big Data while we are still in the gradual phase of the reversal will see your organisation through the reversal.

Big Data is indeed the next big thing in business. Is your organisation equipped to keep up?

What makes for a great User Experience?


User Experience Design is all about creating a platform for better interaction between the user and his/her screen. This interaction is dependent on the type of website, interaction modes, visual language, and of course the users themselves. As a result of these multiple dependencies, there really is no one-size-fits-all model to ensure a great user experience. However, it is possible to point out some salient concepts that all User Experience Designers should incorporate into their tool designs to give their users a hassle-free web experience. Three such concepts are detailed below

Easy Understanding:

A tool provides a good user experience if it is well able to guide the user through itself. The information that the user requires should be laid out with absolute clarity and appropriate hierarchy.

Remember the old design adage: ‘Form Follows Function.’ When you have search boxes that look like buttons, users will tend to keep clicking them, expecting an action. The visual form of your actions should always make clear to the users what those actions relate to. This can be achieved by making things look like their function – for example, 3D looking action buttons, the cursor changing to a little grabbing hand when something that can be dragged is clicked.

Effective Engagement:

Your users are humans. They have a natural propensity for taking to aesthetically appealing and easy-to-use designs. However, to engage users, the tool not only needs to look good and work well, but should also work in interesting ways. Include nice, simple interaction modes to keep your users sufficiently engaged. The interaction modes you include in your tool should not distract the user from the content. After all, a user’s goal is content oriented rather than interface related.

Another factor to consider when designing your tool is your target user’s tastes and moods. Your visual and interaction strategies should be in tune with these aspects. For instance, a comparative overview of MINI’s USA website and Jaguar’s website will readily reveal that these two sites are definitely not intended for the same type of user.

Functional Efficiency:

An efficient User Experience Design lets users accomplish their goals with convenience. Consider the following:

A consumer exploring a product line and considering a purchase has very different goals from, say, an employee working at a customer support call center. The consumer may want to browse through your products, get detailed information about a particular product, or even make comparisons with similar products before making a purchase. A customer support representative, by contrast, will most likely not be looking to explore. S/he clearly knows what s/he needs and is looking to find the relevant information fast.

For a tool to be functionally efficient, it needs to be geared towards addressing your users’ specific goals. This is only possible if you identify your users’ goals first through data analysis or market research, and then craft your User Experience Design strategy accordingly.
Today, web designers are increasingly applying themselves to crafting designs that provide users with the very best surfing experience. Do you use an understandable, engaging, and efficient tool to enhance your users’ web experience?

Seven Simple ways to be Smarter at Reading Data


While sophisticated statistical methods for data-analysis are the current focus of many business discussions, most practicing managers neither have the expertise for such analysis nor the time to learn the methods. And while their data-handing capacities remain limited, the data itself are growing by leaps and bounds. It is tough to handle the pressures of day to day operations as well as make highly informed, data-driven decisions.

Based on my experience in the data-intensive Telecom industry, here are some principles on data analysis which can help you get more insights with no extra training.

1. Keep an Open Mind

This means doubting – ‘good sense’ – which is simply a habit of thinking in a given context. Make it a practice to doubt everything you hear about the business – even from veterans. Articulate the common assumptions and subject them to scrutiny of hard data. Sometimes, they may hold true. But when they don’t, you will end up with useful insights.

2. Predictions First

Before looking at the numbers, make predictions based on your current understanding. Create as many hypotheses as possible. This makes you think rigorously about your ‘world-view’ and uncovers your assumptions about how the business parameters are related to each other. Testing these against the data will will help in sharpening your intuition in the given business environment.

3. Benchmark in the Beginning

Once confronted with a data set, begin by identifying simple benchmarks and descriptive values. Namely, minimum, maximum, and the average. Listing them out separately will help in creating a general ‘sense’ of the data, and help in identifying outliers – which are another source of business insight.

4. Compare, Compare, Compare

The division operator(/) is the magic wand for revealing insights – it essentially means comparing different fields. List out all metrics for your business. Some of these will be primitive – like ‘Total Customers’. Others will be ratios – like ‘Number of Customers per Month’. Your task is to come up with new ratios – compare every parameter with every other parameter. For example, ‘Sales per Salesman’ or ‘Customers per Region’. This can be a huge time consuming exercise – and it is also the most productive.

5. Beware of Averages and Summaries

While they are important to begin with, do not be satisfied with just averages. It is crucial to look at the distribution. Remember the 80-20 rule? Check out how does it apply to your business. Maybe it’s seventy-thirty, or ninety-ten. This helps in identifying the areas you need to focus on, as well as other business trends which are affecting the distribution.

6. Watch out for Spikes – especially the Good Ones

A good principle to follow here is – “If it is too good to be true, it is not true”. Our general tendency makes us feel good when we see great numbers on a parameter, and thus we ignore the details. However, it helps to look under the hood here too.

7. Data and Human Behavior

Beware of the idea that a huge amount of data can tell you all the stories. If you have no context, it will be more than useless – the interpretation will be error-prone. Talk to the people who have been reading the same data for some time to understand the hidden hints, quiz them on the outliers to deepen your comprehension. This will make you better at understanding the behavioral traits underpinning the numbers.

After all, business is more about people, and less about data.

What techniques have you discovered to learn more from your data? How do you reach uncommon insights? Looking forward to your comments.