Blog posts tagged in Big Data

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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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                                            source: parthiansystems.com

                                           

 

What business decisions you make determines the fate of your business. It’s makes enough sense to say that decision making for a business no longer have a reliance only on gut-feeling, past experiences etc since we are now living in a digital-enriched world wherein a huge piles of data is present to make informed decisions. According to Chris Jordan, President, iOLAP, Inc, “Success of any business has a lot to do with unlocking the value of data and convert it into trusted information for critical decision making.”

 

Since massive amount of data from disparate sources exists in structured, semi-structured and unstructured form,  data integration plays an imperative role to turn it into trusted information.  

Why Data Integration Holds Significant Importance for Businesses

 

Data integration helps in converting data from different sources into a structured set of information to make it usable for end-users without complex coding. It allows chief managers to gain insight into customer’s behavior and devise effective marketing strategies.

Benefits of Data Integration

 

  • Creates value for your business
  • Improves business intelligence
  • Allows timely access to business data
  • Allows integration of diverse businesses and their processes through shared data
  • Enables chief managers to meet business goals
  • Helps in creating valuable database
  • High ROI                                      Data_Integration_Acceleration.png

                                                       source: superdevelop.com

                                   

 

Data lake is a term referred to a gigantic amount of data which has not been transformed into meaningful information yet, thus brings no benefit for the holder. However, data integration tools, like Pentaho, help convert it into streamlined data refinery with ease to enable chief managers to make informed decisions. Data integration software hold the key for business managers to make predictions, improve business operations and offer products to customer that fit in their interest areas.

 

Data integration tools enable ETL (extraction-transform-load) to process big data sources in familiar ways. Moreover, data integration tools give you access to their big library of pre-built components, which make it easier for you transform data from a full spectrum of sources. Sub-areas of data integration comprise of data warehousing, data migration, enterprise application and master data management.

Below is the list of 10 popular data integration tools  

 

  • Astera

  • IBM’s Agile Data Integration
  • Pentaho
  • SAP
  • Jitterbit
  • Oracle Data Integration
  • Informatica
  • HVR Software
  • Information Builders
  • Adeptia

 

Data integration also gives rise to Predictive personalization, which is the new mantra for businesses to increase their sales numbers and keep on adding new customers to the list of loyal customers. It helps you create a good brand value in the market as invokes a feeling among customers that you pay heed to their individual preferences and deliver tailor-made solutions. CRMs, social media, websites, news feeds etc. generate a lot of data about customers, but without data integration, it’s not possible to make sense of it and figure out what sort of personalized experience your prospective customers are demanding from you. Data integration tools do not cost much and take less time to provide you vital information to transform your business into a big success.

 

Exploring highly interactive and high-performance information via data integration opens gates to take informed business decisions and take your business to new heights.

 

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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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