Blog posts tagged in Big Data Analytics

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No matter what type your business is, data analysis always plays a vital role in providing you a competitive edge and bring innovation in the way you do your business. Collating data to discover new business opportunities and meet customer expectations is not something very new, dating back to the decade of 70s. However, what has significantly changed since then is the amount of data growing everyday, its diversity and analytics techniques to make sense out of it.

The term Big Data was coined considering the abundance of data that exists today in structured, semi-structured and unstructured form. According to a new report from IBM Marketing Cloud, 90% of data that exists today was created in the last two years alone. The report further adds that nearly 2.5 quintillion bytes of data is created every day from numerous sources, like social media sites, business apps, public web, sensors connected to Internet of Things, etc.

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In contrast to previous times when rudimentary data analytics was used to make future decisions, advanced big data analytics of today allows business managers to have real-time information and make immediate decisions. There is no denying the fact that big data analytics has become the need of the hour to understand hidden patterns and correlations, identify target audience, uncover new revenue opportunities, boost marketing efforts, offer better customer service, and improve productivity. Let’s understand in detail how information derived from big data analytics can help you get competitive advantage over your rivals.

How Big Data Analytics enables You to Beat Competition

Allows to offer Personalized Experiences

Acquiring customers is not enough to compete with your rivals. You also need to understand the needs of your customers and optimize their buying experience. Since customers interact a lot on the internet and look for products they want to buy in near future, big data analytics allows you to identify them and offer them what they want, not something they are not interested in. Doing so increases the chances of a customer to buy a product of their choice from you by many folds.

Having a good insight into a customer’s personality, attitude, buying behavior, etc. helps you make them feel personally valued, which increases their trust in your brand. Having valuable information of customers readily available paves the way for business managers to devise effective strategies and improve conversion rates. If you have a website for your business, you can use big data analytics to personalize the content in real time to meet diverse requirements of consumers based on their sex, nationality, etc.

Click here to read about “How Will Big Data Impact Effective Lead Generation for better Conversions.”

Enhances Productivity

Big data analytics also helps organizations enhance productivity. For good productivity, it’s important to understand production volume patterns, correlation with demands, and patterns from unstructured data, like social media feeds, blogs, videos and other sources. With big data analytics, it becomes possible for organizations to get useful insights from both structured and unstructured data, which in turn helps expedite varied processes and boost productivity.

Besides, many organizations are now using big data analytics to analyze employees’ performance. As a result, they are able to improve office productivity. By using devices like motion sensors, employers can keep a track of employee activity and understand them better, which in turn helps managers devise strategies to encourage them and improve productivity.

According to a study by Evolv, data analysis has proven to be a great way for getting insights into employees’ behavior and increasing productivity in the workplace. Max Simkoff, co-founder and CEO of Evolv, says, “CIOs have been charged with leveraging the right products to increase employee productivity. Unfortunately, much of that work revolved around guessing what employees wanted and what they didn't. With help from big data, it appears that far more insight can be gleaned from employees’ behavior and, in turn, improve their job performance.”

Helps Understand What Works for You and What Doesn’t

One of the best advantages of big data analytics is to be able to learn about the efficacy of your marketing efforts. For example, marketers can determine which sort of content has been most effective at moving leads through a marketing and sales funnel. Similarly, they can also deduce what’s not working for them. Therefore, business managers can devise marketing strategies based on what’s most likely to become propitious for them and boost ROI.

Small businesses can also see how popular their brand is becoming among customers and keep on improving their products or services to keep growing the list of loyal customers. One of the major reasons cited by most businesses to use big data analytics is to improve business operations. If there’s an operational problem, a real-time insight into errors will help take quick actions to fix it, thereby not allowing the operation to fail or force your customers to go somewhere else.

Reduces Business Costs

Big data analytics also helps businesses save significant costs. By putting big data analytics into use, the supermarket chain Tesco successfully brought down its annual refrigeration cost by 20% across 3,000 stores in the UK and Ireland. With the help of IBM's research laboratories in Dublin, Tesco got useful insights into gigabytes of refrigeration data, which helped the company uncover that refrigerators in many of their stores in Ireland were running at lower temperatures than needed.

Niall Brady, IBM's research engineer for intelligent buildings and energy analytics, said, “We developed a set of key performance indicators [KPIs] that looked at data aggregated every month over a year. Without knowing anything about how the refrigerators should perform, we could identify when they were behaving in an anomalous way.”

In a nutshell, big data analytics has enough potential to serve the purpose of automated decision systems to let managers know any cost-cutting opportunity and swiftly act on it.

Helps Improve Risk Management

Big data analytics enables to not just offer better products and reduce costs, but manage operational, technical and financial risks as well. Hacks, frauds, policy breach, etc. are some of the risks that could be taken care of by putting big data analytics into use. Unlike previously when it was difficult to analyze fraud patterns, identify unscrupulous traders, etc, it’s now possible to have highly effective risk management programs by using big data analytics.

Financial institutions are at the highest risk of frauds, which is one of the major reasons why most banks now use big data analytics to monitor all transactions in real time and preempt suspicious activities. CFOs across many sectors now rely on big data analytics for efficient risk monitoring and prepare for worst-case scenarios. Palantir Technologies, a data analytics company which has clients including the FBI and CIA, is also helping Wall Street firms to detect frauds. The crux is the fact that big data analytics could definitely provide you a competitive edge by allowing you to manage risks and prevent money loss in a much better way.

For big data analytics, there are many tools available in the tech market. You can choose the one that best fits your requirements. To ease your quest, you can click here to check out “7 Tools for Successful Big Data Analytics.

Considering all the virtues of big data analytics mentioned above, it’s worth saying that businesses can reap many benefits that were not possible before. Data is like currency, but it’s only useful when you can make sense out of it and incorporate the wisdom earned into your business processes, marketing campaigns, customer service strategies, they way you react to changing trends, etc. It’s no hyperbole to say that big data analytics is the key to outperform competition and cash in on new opportunities of growth.

What are your thoughts on the potential of big data analytics to help businesses get a competitive edge? Have you ever used big data analytics before to give a boost to your business? Your views are highly welcome, please share them in the comment box below.

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The term Big Data has created a lot of hype already in the business world. Chief managers know that their marketing strategies are most likely to yield successful results when planned around big data analytics. For simple reasons, use of big data analytics helps improve business intelligence, boost lead generation efforts, provide personalized experiences to customers and turn them into loyal ones. However, it’s a challenging task to make sense of vast amounts of data that exists in multi-structured formats like images, videos, weblogs, sensor data, etc.

In order to store, process and analyze terabytes and even petabytes of such information, one needs to put into use big data frameworks. In this blog, I am offering an insight and analogy between two such very popular big data technologies - Apache Hadoop and Apache Spark.

Let’s First Understand What Hadoop and Spark are?

Hadoop: Hadoop, an Apache.org. Project, was the first big data framework to become popular in the open source community. Being both a software library and a big data framework, Hadoop paves the way for distributed storage and processing of large datasets across computer clusters using simple programming models. Hadoop is a framework composed of modules that allow automated handling of common hardware failure occurrences.


The four primary modules that comprise Hadoop’s core are:



Hadoop is a file system with a two-stage disk-based compute framework MapReduce and a resource manager YARN. Apart from Hadoop’s core modules, there are several others in existence as well, including Hive, Pig, Ambari, Avro, Oozie, Sqoop and Flume. These modules are also well capable of working with big data applications and processing large data sets.

The main motive behind designing Hadoop was to look through billions of pages and collect their information into a database. And, that gave birth to Hadoop’s HDFS and its distributed processing engine, MapReduce. Hadoop is a great help for companies that have no effective solution to deal with large and complex datasets in a reasonable amount of time.


Apache Spark: Spark, also an open-source framework for performing general data analytics on distributed computing cluster, was originally designed at the University of California, and later donated to the Apache Software Foundation. Spark’s real-time data processing capability provides it a substantial lead over Hadoop’s MapReduce.

Spark is a multi-stage RAM-capable compute framework with libraries for machine learning, interactive queries and graph analytics. It can run on a Hadoop cluster with YARN but also Mesos or in standalone mode. Apples and oranges, really. An interesting point to note here is that Spark is devoid of its own distributed filesystem. So, for distributed storage, it has to either use HDFS or other alternatives, such as MapR File System, Cassandra, OpenStack Swift, Amazon S3, Kudu, etc.

Now that we have caught a glimpse of Hadoop and Spark, it’s time to talk about different types of data processing they perform.

What are Different Types of Data Processing?

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There are three types of data processing: Batch Processing, Stream Processing and Hybrid Processing.

Batch Processing: Batch processing has been pivotal to big data world for years now. The simplest way we can define batch processing is operating over high volumes of data collected over a period of time. Since data is first collected, entered and then processed, results are produced at a later stage. Although batch data processing is an efficient way of processing large, static datasets, the time taken to return the result is long as it happens only after the computation is complete.

Nevertheless, batch processing is the best for holistic treatment of datasets. For example, when access to a complete data set is required, like calculating totals and averages, there is no data processing more suitable than batch processing.

Click here to learn more about batch processing

Stream processing: Stream processing has become the current trend in the big data world. The modern business era is about speed and real-time information, which is what steam processing is the most suitable for. Since batch processing does not allow businesses to react to changing business conditions in real time, stream processing has witnessed a rapid rise in demand in past few years.

Although stream processing systems can also handle vast amounts of data, they operate over one or micro batches at a time. According to Mike Gualtieri, an analyst at Forrester Research, “With traditional analytics you gather information, store it and do analytics on it later. We call that at-rest analytics.” However, streaming technologies allow analysis of a series of events that have just happened. “It could be a piece of farm equipment that has a lot of sensors on it emitting data on temperature and pressure. You want to analyze that in real-time to see if there is a risk of the engine blowing up.”

Click here to take a deep-dive into stream processing of big data

Hybrid Processing: Hybrid processing is nothing, but the capability of a processing system to perform both batch processing and stream processing.

Comparison Between Apache Hadoop and Apache Spark

Data Processing

Hadoop: Apache Hadoop provides batch processing. In fact, Hadoop was the first framework that created ripples in the open-source community. Google’s revelation about how they were working with vasts amounts of data helped Hadoop developers a great deal in creating new algorithms and component stack to improve access to large scale batch processing.

MapReduce is Hadoop's native batch processing engine. Several components or layers (like YARN, HDFS etc) in modern versions of Hadoop allow easy processing of batch data. Since MapReduce is about permanent storage, it stores data on disk, which means it can handle large datasets. MapReduce is scalable and has proved its efficacy to deal with tens of thousands of nodes. However, Hadoop’s data processing is slow as MapReduce operates in various sequential steps.

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Image source: zData Inc

Spark: Apache Spark is a good fit for both batch processing and stream processing, meaning it’s a hybrid processing framework. Spark speeds up batch processing via in-memory computation and processing optimization. It’s a nice alternative for streaming workloads, interactive queries, and machine-based learning. Spark can also work with Hadoop and its modules. The real-time data processing capability makes Spark a top choice for big data analytics.

Resilient Distributed Dataset (RDD) allows Spark to transparently store data on memory, and send to disk only what’s important or needed. As a result, a lot of time that is spent on the disc read and write is saved.

Ease of Use

Spark is easier to use than Hadoop as it comes with user-friendly APIs for Scala (its native language), Java, Python, and Spark SQL. Hadoop, on the other hand, is written in Java, difficult to program and requires abstractions. Since Spark provides a way to perform streaming, batch processing and machine learning in the same cluster, users find it easy to simplify their infrastructure for data processing.

An interactive REPL (Read–eval–print loop) allows Spark users to get instant feedback for the commands. Although there is no interactive mode available with Hadoop MapReduce, tools like Pig and Hive make it easier for adopters to work with it.

Graph Processing

Hadoop: Most processing algorithms, like PageRank, perform multiple iterations over the same data. MapReduce reads data from the disk and after a particular iteration, it sends results to the HDFS and then again reads the data from the HDFS for next iteration. Such a process increases latency and makes graph processing slow.

In order to evaluate the score of a particular node, message passing needs to contain scores of neighboring nodes. And, these computations require messages from it neighbors, but MapReduce doesn’t have any mechanism for that. Although there are fast and scalable tools, like Pregel and GraphLab, for efficient graph processing algorithms, they are not suitable for complex multi-stage algorithms.

Spark: Spark comes with a graph computation library called GraphX to make things simple. In-memory computation coupled with in-built graph support allows the algorithm to perform much better than traditional MapReduce programs. Netty and Akka make it possible for Spark to distribute messages throughout the executors.

Fault Tolerance

Hadoop: Hadoop achieves fault tolerance through replication. MapReduce uses TaskTracker and JobTracker for fault tolerance. However, TaskTracker and JobTracker have been replaced in second version of MapReduce by Node Manager and ResourceManager/ApplicationMaster, respectively.

Spark: Spark uses RDD and various data storage models for fault tolerance by minimizing network I/O. In the event of partition loss of an RDD, the RDD rebuilds that partition through the information it already has. So, Spark does not use the replication concept for fault tolerance.

Security

Hadoop MapReduce has better security features than Spark. Hadoop supports Kerberos authentication, which is a good security feature but difficult to manage. Hadoop MapReduce can also integrate with Hadoop security projects, like Knox Gateway and Sentry. Third party vendors also allow organizations to use Active Directory Kerberos and LDAP for authentication. Hadoop’s Distributed File System is compatible with access control lists (ACLs) and a traditional file permissions model.

Spark’s security is currently in its infancy, offering only authentication support through shared secret (password authentication). However, organizations can run Spark on HDFS to take advantage of HDFS ACLs and file-level permissions.

Costs

Both Hadoop and Spark are open-source projects, therefore come for free. However, Spark uses large amounts of RAM to run everything in memory, and RAM is more expensive than harddisks. Hadoop is disk-bound, so saves the costs of buying expensive RAM, but requires more systems to distribute the disk I/O over multiple systems.

As far as costs are concerned, organizations need to look at their requirements. If it’s about processing large amounts of big data, Hadoop will be cheaper since hard disk space comes at a much lower rate than memory space.

Compatibility

Both Hadoop and Spark are compatible with each other. Spark can integrate with all the data sources and file formats that are supported by Hadoop. So, it’s not wrong to say that Spark’s compatibility to data types and data sources is similar to that of Hadoop MapReduce.

Both Hadoop and Spark are scalable. One may think of Spark as a better choice than Hadoop. However, MapReduce turns out to be a good choice for businesses that need huge datasets brought under control by commodity systems. Both frameworks are good in their own sense. Hadoop has its own file system that Spark lacks. And, Spark provides a way for real-time analytics that Hadoop does not posses.

Have you ever got a chance to use any of the two frameworks for big data applications? Do you think Spark can replace Hadoop in the future? As always, your views are vital for all our readers, please share them in the comment box below.
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Data collection and analytics have always been crucial to chief business managers’ capability of making right business decisions. But unlike past, databases now have data with high volume, velocity, and veracity. Going by a big data infographic contributed by Ben Walker of Voucher Cloud in 2015, around 2.5 quintillion Bytes of data is created every day. The amount is good enough to fill 10 million Blu-ray discs.


Given the gigantic amount of data existing in databases nowadays, data industry coined a new term for it - Big Data. Big Data is basically large volumes of information present in databases in structured, semi-structured and unstructured form.


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A lot of hype has already been created around big data as its analysis opens new avenues for business managers to boost sales by targeting or retargeting right customers. Big data analytics helps understand what customers want to buy and what they don’t like about your products or services. Therefore, you can figure out a quick fix and improve the brand value of your business. Besides, you can provide personalized experiences and add more numbers to the list of loyal customers.


However, an important point to note here is that making sense of big data is a very challenging task. That said, one needs to put into use an analytics tool to make sense of big data and turn it into significant business value. Let’s discuss 7 tools business managers can use to work with big data for successful analytics.

7 Tools for Big Data Analytics

#1 Hadoop

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Apache Hadoop is an open-source software framework that facilitates distributed processing of very large data sets across hundreds of inexpensive servers that operate in parallel. It’s been quite a time business have been using Hadoop to sort and analyze big data. Hadoop uses simple programming models to ensure distributed processing of large data sets and making them available on local machines.


Click here to learn more about Hadoop

#2 Storm

Storm, another product from Apache, is a real-time big data-processing system. Storm is also open source and can be utilized by both small and big businesses. It is fault tolerant and goes well with any programming language. Storm is capable of performing data processing even if any of the connected nodes in the cluster die or messages are lost. Other tasks that Storm can perform is distributed RPC and online machine learning. Storm is a good choice for big data analytics as it integrates with existing technologies, which makes processing of big data much easier.


Click here to learn more about Storm

#3 Hadoop MapReduce

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Hadoop MapReduce is a programming model and software framework for writing data processing apps. Originally developed by Google, MapReduce enables quick processing of vast amounts of data in parallel on large clusters of compute nodes.


The MapReduce framework has two types of key functions. First, the map function which separates out data to be processed, and second, the reduce function which performs data analysis. As MapReduce involves two stage processing, it’s believed that a large number of varied data analysis questions can also be answered with it.


Click here to learn more about MapReduce

#4 Cassandra

Apache Cassandra is highly scalable NoSQL database. It is capable of monitoring large sets of data spread across large clusters of commodity servers and the cloud. Cassandra was initially developed at Facebook out of a need for a database to power their Inbox Search. The big data tool is now widely used by many famous enterprises with large, active datasets, including Netflix, eBay, Twitter and Reddit.


Click here to learn more about Cassandra

#5 OpenRefine

OpenRefine (formerly GoogleRefine) is an open source powerful tool that is meant to work with messy data. The tool allows quick cleaning of huge sets of messy data. Then, it transforms the data into useable format for further analyses. Even non technical users can integrate OpenRefine into their data workflow at ease. OpenRefine also enables to create instantaneous links between datasets.


Click here to learn more about OpenRefine

#6 Rapidminer

Rapidminer is an open source tool that is capable of handling unstructured data, like text files, web traffic logs, and even images. The tool is basically a data science platform that relies on visual programming for operation. With Rapidminer comes functions that include manipulation, analysis, modeling, creation of models, and fast integration in business processes. Rapidminer has become popular among data scientists as it offers a full suite of tools to help make sense of data and convert it to valuable business insights.


Click here to learn more about Rapidminer

#7 MongoDB

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Image courtesy: devGeeK

MongoDB is an open source and widely used database for high performance, high availability, and easy scalability. It is classified as a NoSQL database. MongoDB’s distributed key value store, MapReduce calculation capability and document oriented NoSQL features make it a popular database for big data processing. MongoDB is well suited for programming languages like JavaScript, Ruby and Python. MongoDB is easy to install, configure, maintain and use.


Big data analytics has become the need of the hour for business managers to make smarter business moves and yield higher profits. However, without a big data analytics tool, it’s very difficult to uncover hidden patterns, correlations and other insights to get a competitive advantage and take your business to new heights. With this, I am wrapping up this blog, hoping it helps you choose a big data analytics tool that suits your business the best.

Do you have a firsthand experience of using any big data analytics tool? Or, do you want to add more to what’s already being discussed above? As always, your views are vital for all our readers, please add them in the comment box below.

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Retail merchandising strategy is paramount for small and medium sized retailers to boost sales of their products. It’s imperative to explore every corner of business strategy landscape to survive tough competition. For example, determining the place where a particular type of product needs to be placed so that maximum prospective customers can see it with ease, which can be the difference between a sale and non-sale.

Retail merchandising involves:

  • Launching right product

  • Setting right price

  • Positioning Products at the right places

  • Placing products in right amount

  • Knowing types of customers buying a particular product

  • Drawing traffic to particular products

  • Improving customer relationship

  • Buying trend

The problem is that with all this, there comes a huge volume of disparate data, which is basically useless if one can’t run proper analytics on it to figure out sales patterns, optimum positioning and buying behaviours of customers and perhaps more importantly so, the prospects.

Use of analytic tools could do wonders for small and medium sized retailers. Many of them often struggle to come up with an effective merchandising strategy because of being deprived of information they can extract from their own databases, forget about the vast amount of useful data floating around on the interweb.

Social media, industry forecasts, existing customers records and web browsing patterns can help retailers predict products a specific segment of customers is more likely to buy. For example, Kohl’s had announced personalized offers for customers in five of its stores. Smartphones were all required for customers to opt for the offer while they visited one of those stores. A customer who had looked for a pair of shoes online but never went ahead with the purchase would receive a coupon based on the same shoes. This had increased the chances of the sale of the shoes for Kohl’s by many folds, as customers have a very high likelihood to avail an offer when they get it at the time of purchase while they are shopping.

With increasing use of the internet on cellphones worldwide, experts predict that 25% of the world will be soon on social network. This creates big opportunities, but simultaneously, it also creates problems owing to unstructured, semi-structured and muddled nature of data. Here pops up a question how to use big data to help small and medium retailers devise marketing strategies that improve customer experience, boost sales, understand buying trend inside a retail outlet etc.

But first, let’s get some concepts right about Big Data. Let’s start with the three Vs of big data - Volume, Velocity and Variety.

Volume - Nowadays, a lot of data is available in the form of videos, musics and large images on social media channels. The volume is so large that normal computer systems are incapable of processing it.

Velocity - Data movement has become very fast. Gone are the days when data of 24 hours ago was considered recent. Now, people don’t rely on newspapers to stay updated, they rather get the latest news through social media, which even tells you what happened half an hour ago. Updates are now made almost every second as data is being accumulated across the world on various platforms. This fast movement of data represents big data.

Variety - Data is available in many formats, like database, excel, csv or access.  It’s sometimes even available in the the form of video, SMS, pdf etc. It’s a big challenge with big data to arrange data available through different formats in one format.

IBM is among many companies that offer big data solutions to retailers to help them devise personalized marketing campaigns. IBM’s big data solution helps retailers understand customer shopping behavior, improve cross-selling & upselling, analyze product and customer data to avoid stock-outs and overstocks etc.

However, these full-fledged big data solutions are very expensive for small and medium sized retailers. The best remedy to reduce high costs of big data solutions is to go for customized solutions. Evon Technologies offers such custom-made big data solutions to retailers at very nominal prices, thereby providing them an affordable way to make their business more agile and robust. Having a tool to understand big data is next frontier for small and medium sized retailers in order to ensure their survival amid cut-throat competition.  


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A lot of songs have been sung about the virtues of having precise information at the precise time. And these songs just don’t get old. If anything, they are only getting BIG.

 

People are looking for information (read products and services) all across the web. You, as a salesperson might have an offering but the problem is - what are the chances that the person interested in it will find you and reach across to you? Frankly, the chances are quite less. So what do you do to increase your chances to make a sale? Well, obviously the best thing you can do is to find and reach across to that person before he decides to give his money to someone else. But how to do that? Traditional lead generation methods are only so effective as to give you an excuse of an alternate to shooting in the dark. The generated lead data is limited, the windows are short, the targets are big, the work is harder and the results are uncertain. The conversion rates can well be compared to the conversion rate of a toiling army of bees for one drop of honey.

 

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A decade ago, most salespeople would agree that the traditional methods only took them so far in terms of conversion rates. The data was too limited or redundant and took too long to accumulate but the silver lining, if we can call it that, was that because it was too little, it was easy to process. You got 30 leads, you go and do your salesperson thing with 12 based on some quick prospecting/scoring and depending on how good or lucky you are, you score a couple.

 

Then five years ago to until recently, salespeople were agreeing that the contemporary methods with the power of web and social media, brought improved capabilities in data acquisition and reach but still something was keeping them from milking that cow. You’d think with all that talk about shrinking degrees of connection, businesses increasing their online presence and all, you’d be better off than mere 3% growth.

 

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Yes, something was definitely missing from the picture. And that something was to do with this - “Having access to a lot of data means nothing if you don’t have a way to utilise it...to its full potential.”

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Hmm…”utilizing”, people thought. And then they thought of newer ways to do that. New buzzwords started cropping up - Mining, BI, Analytics. But while that was happening, the data kept spawning silently, persistently and exponentially. And by the time the Sales teams settled on their Analytics tools, they found to their utter despair that they weren’t enough anymore to handle the Volume, Velocity and Variety of data that has been piling up all that while. That almost took the whole bang out from the so called data-explosion. Fortunately, that didn’t happen. Especially, in our case, for the Modern Salesperson.

 

The modern salesperson, despite having the same problems (perhaps even Bigger), are agreeing, either reluctantly or expectantly to one thing - that a major paradigm shift in the way information is produced and consumed has been set in motion for some time now, that there is an enthusing buzz in the air and that that buzz seems to hold a Big promise!

 

Big Data Promise and The Age of Proactiveness

 

There’s lots and lots of data floating around the web holding immense potential information for you as a sales person, if only it can be churned to your benefit somehow. But given the speed at which this data is getting generated and becoming obsolete, even the first step can become overwhelmingly discouraging. That first step is - to capture this huge amount of data in one place. But then, the tougher part comes next -  to make it sensible and actionable. For a salesperson, this sensible and actionable information is what he calls a Lead.

 

So how does Big Data help or proposes to help? Well to start with, Big Data Solutions solve this problem of getting you actionable leads by helping you with at least four things making your chances to conversion far better than those of that salesman a decade ago. These are:

 

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  • Identifying most valuable potential customers and creating windows of opportunities
  • Telling you the precise thing to show or say to them when the window opens
  • Have the right thing to offer at the right time to your prospect
  • Raising right flags at the right moment to generate cross-selling and/or up-selling opportunities 

 

 

Big Data Impact on Sales

 

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Companies collect a lot of data through a wide array of channels like mobile, website tracking/analytics tools, contact forms, social media, lists, groups & forums, CRM systems and news feeds. While big companies prefer to use their custom developed or customized Acquisition and Analytics solutions by Big Data solution providers like IBM (BigInsights), Cloudera and HortonWorks; most companies (SMBs mainly) prefer to source their data from a new breed of service providers falling under DaaS (Data as a Service) category who provide On-Demand industry-wise, rich, hard-to-find-data of personnel who can be potential clients. This data is then imported into organizations CRM systems from where the analytics and further lead nurturing process is taken up. Or some prefer to go for the simplest of the solutions - "Outsource" the whole lead generation process to companies like Technology Sales Leads (www.tslmarketing.com), let them deal with the grind and hope to get valuable leads.

 

HadoopAnyway, let’s take a moment to see how the actual data acquisition works in terms of Big Data in general. Well, it’s usually done using the combination of traditional, contemporary and modern methods using techniques like manual and/or automated web content mining, data scraping, searching, social media profiling and crowdsourcing. This data is usually in an unstructured form and is constantly fed and processed into what we call in Big-Data terminology as data-sets using technologies like Hadoop. 

 

 

However (can’t stress this enough), just acquiring a lot of data isn’t good enough, for the simple reason that due to its muddled and voluminous nature, it is of little value in itself. To make some sense out of it requires a lot of sifting through, filtering, consolidating, cleansing and validating. And because this effort requires time, using traditional (slower) approaches, it’s more prone to become counterproductive, especially in case of Sales because from Sales perspective, the long exercise might lead to generating more cold leads than any useful ones, as data keeps coming in and changing at a rapid rate and has the tendency to become obsolete fast.

 

So it becomes imperative to find a way to do it in a more efficient and productive way. One way to do it by having a tool or a system to do this crunching and churning for you - and giving you a streamlined and consolidated picture of what the above systems are feeding you with. But given the big volume of such acquired data, managing it and running complex analytics queries on it becomes a challenge with traditional RDBM systems. And that’s where the Big Data guys come in. Companies like Oracle, Cloudera, Hortonworks, IBM, Intel, Microsoft, and many others all have identified the potential of a solution to this Big Data problem and have come up with their own versions of Big Data Analytics solutions.

 

In our graphic, this whole thing is happening at stage 2.

 

Once you have the targeted leads, the usual Sales Process takes over, the only difference is that since the lead generation, prospecting and scoring has been mostly taken care of by the system, you as a Sales person hit the ground running armed with exact information of who to contact to, what to offer him and when.

Start Well to Finish Well 

 

One of the big advantage that these solutions offer is the range of Analytics one can perform over a large amount of data in a quick and visual (graphs, charts, tables) way. If we take our case of Big Data application vis a vis Sales Process, the direct implication is the shortening of the traditional long-tailed lead nurturing and lead scoring processes by doing the dirty mining work and handing over targeted insights based on your specific criteria (like industry vertical, company size, company revenue, location etc). This ultimately allows a Salesperson to filter out the weak leads and focus on nurturing only the valuable leads (graphic: Stage 7), the ones which have the greatest chance of conversion to Actual Sales.

 

The beauty of the system is that at every step, new transactional data (financial, logistical, communications etc) is getting generated and getting fed-back into the system which in turn helps in the process of generating repeat, cross-selling and/or up-selling opportunities. Talk of eating your cake and having it too!

 


 

Evon Technologies is a software consultancy based in India and has performed Proof of Concepts for data mining companies with Data-Integration and Hadoop Analytics requirements.

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