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Sachin Khare

enterprise-analytics

Enterprise Data Analytics: Making data driven decisions

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It’s no accident that industry increasingly depends on deep analytical talent. Our world is being reshaped in profound ways as Big Data remakes entire industries in its own image. Really, it’s difficult to overstate how “big” Big Data’s influence is turning out to be.

Big data is impacting almost every major industry with the ever-improving capabilities and amount of data that they’re collecting from sensors, online and other online sources.

Big Data is getting bigger. In its wake, we’re all looking for ways to understand it and put it to use. In order to talk about the Big Data phenomenon, we first have to consider what brought us here in the first place. From there, we’ll gain a better perspective of where we’re going.

After a very brief overview of data analytics and Big Data, we can start talking about practical applications for industry. Big Data can be very useful if you know what to do with it.

Data Overload: Why Big Data’s So Big

Key technological advances are converging now to create a lot of Big Data. A century or two ago, people might’ve thought of a single book as a useful measurement for information’s size. Libraries carry a lot of books, so they have a lot of data. This worked pretty well for most applications until computers and data science became more comfortable with petabytes:

  • Megabyte: A book
  • Gigabyte: A shelf of books
  • Terabyte: A library
  • Petabyte: 1,000 libraries
  • Exabyte: 1,000,000 libraries

An Exabyte is truly a lot of data. Then again, there’s also a lot of data to measure. Pretty soon, we’ll have to move on to even bigger measurements.

One widely cited statistic from IBM Marketing Cloud claimed in 2016 that 90 percent of the world’s data was created within the previous two years. The Internet’s traffic in a year was said to equal several hundred exabytes back in 2012, and it’s a lot bigger now.

Increasingly, the world’s data is being collected and stored without an immediate application; we collect it now and ask questions later. Big Data, it seems, is always getting bigger–we don’t entirely always know what to do with it.

The way we got here is a bit complicated. Essentially, computers became much better and the Internet of Things (IoT) accelerated some important trends already happening in data collection, storage, management, and use.

There’s more data to collect, but there’s also more reasons than ever to collect it and more ways to get it done. As a consequence, we’re experiencing something akin to data overload. But, naturally, there’s more to the story than too much data.

Distinctions Between Big Data and The Traditional Data Ecosystem

Big Data is more than just a huge amount of traditional data. It’s makeup and behavior is often completely different from anything resembling the traditional data ecosystem.

There’s this perception that Big Data and Data Analytics are just new words for old ideas. That couldn’t be further from the truth, however. Big Data is on everyone’s radar today because Big Data happens to take on very different characteristics and behaviors.

Just like the unique transformation the laws of physics demonstrate at the quantum level, data gets really weird when it gets really big. The world of Big Data is quite a bit different, it turns out, from how traditional data looks. That fact is creating a whole new Big Data ecosystem and it’s even working to transform our global economy.

Let’s consider these defining characteristics of Big Data, for instance:

  • Data collection often starts without a hypothesis, plan, or theory
  • The volume of data increases exponentially as data collection continues
  • Often lacks structure
  • Resource-intensive to work with
  • There’s a high rate of change requiring some dynamic analysis
  • It’s not all useful
  • At least some of it is a complete mess

If we can understand and become intimately acquainted with the data set we’re looking at, there’s usually a story to tell. But is it worth telling?

Not always. But here’s how to find out.

Analysis and Big Data

Storing and Accessing Big Data for Analytics

All of this data is only useful if we can store and access it. In a nutshell, data must be collected, pre-processed, and stored. It often has to be cleaned before much processing can be done. So, there’s definitely a lot of work to do before analysis can begin.

Before we can begin to use a data set, we have to answer pre-processing questions such as:

  • Is this error-free, or can we economically remove the errors?
  • Is the data accurate?
  • Do we need more data, or do we have enough?
  • How much work do we need to do before the data’s in good enough shape to use?

While we’re considering these questions, it helps to have a model for approaching the data set. And once we start developing a model, it’s important to have a sandbox where we can test it out before trying it on the actual data.

After that step, we’re ready to start analysis. Now, let’s get down to the good stuff–data analytics.

Types of Data Analytics

There are different ways to approach data once we have it, and we don’t always initially know what the best type of data analytics will be until we start looking at the actual data. If you don’t know what to do yet, there are two basic strategies you can try:

  • Exploratory Data Analysis: Data mining to determine what methodology to apply
  • Repetitive Data Processing: By systematically trying a selection of different methods, we can try to find the right approaches to use

From there, it’ll gradually become clear what a data set needs. Here are just a few different approaches:

  • Descriptive: Take the data and create a summary of what happened. Useful for social data.
  • Predictive: Using different models, statistical analysis, machine learning, etc, you start looking for predictions or trends. This is probabilistic in nature.
  • Prescriptive: A type of predictive analytics, prescriptive analytics creates actionable insights. Good for a lot of business data uses.
  • Heterogeneous analytics: Starts with where it wants to end, then works backwards to get there. Useful for making business decisions.

With the right analytical techniques, we can start making use of all of that data.

Analytical Techniques for analysis and visualization

There are several different analytical techniques, such as forecasting, regression, and statistical analysis. Once we know what to do with the data, it’s time to begin doing something with it. With machine learning, this is in some respects becoming easier to do.

Large data-sets can be trickier to work with, but thanks to smart analytical techniques, we can use this data in interesting ways.

For instance, these analytical techniques are creating tremendous value for industry, even with Big Data.

Classification Tree Analysis

Sometimes, we’re presented with new data we can classify based on what we’ve observed before with older data. Using a historical data set, we can tag new data into categories.

Some basic examples:

  • Building customer profiles with help from old behavior and transaction data
  • Reviewing and categorizing documents

Regression Analysis

Best used with quantitative data, regression analysis allows us to study the relationship between an independent variable and a dependent variable. These relationships can be particularly interesting in business, for instance:

  • Call center wait times and number of complaints
  • Temperature baked goods are produced at and product shelf life
  • Extended shopping hours and sales totals

The conclusions we can glean from regression analysis help us make better business decisions, improve operational efficiency, and decide which risks are worth taking as an organization.

Predictive Forecasting

A really common form of data analytics; many businesses use predictive forecasting without even thinking about it! Using trends, cycles, or notable patterns in past data, predictive forecasting makes predictions about what future trends will happen when more data is collected.

Everyday examples:

  • A sales team forecasts their next quarter’s sales numbers using previous quarters’ data
  • A warehouse plans to order a bigger shipment for next month based on last year’s sales
  • VoIP (Voice over Internet Protocol) phone service plans are adjusted in time for higher call volumes during a holiday season, based on past years’ service needs

Of course, there are many ways we can study data and apply analytics techniques. Recently, AI technologies have grown to play a bigger role, too.

Analytics and Emerging Technologies

With Big Data, we often have large data sets that don’t easily and immediately conform to straightforward data analysis techniques. Thankfully, AI and machine learning are coming to the rescue.

Machine learning can help us sift through extraneous data and find only the important stuff. Using an initial set, machine learning can adapt and computers can learn to recognize the important and useful so the bad data is safely excluded.

Bigger data questions, like these, can be addressed with help from emerging tech:

  • The probability of winning legal cases
  • How to identify spam email
  • How to safely navigate a self-driving car

How Data Analytics Helps Industry

forbes-report

Credit: Forbes report on how big data and analytics have significantly influenced the industry

Data analytics has a wide range of uses in industry, as one can imagine. With the right insights, businesses are able to make more effective decisions and manage uncertainty. Having methodology to effectively use Big Data is allowing more industries to benefit from today’s data overload.

Recent corporate case studies from Datameer, for instance, have cited some pretty significant results:

  • Video Gaming: One gaming company doubled revenue to over $100 million after implementing behavioral analytics to help with monetization of the platform.
  • Retail: Another company used data from social media profiles and behavior to target their customers with more appropriate offers and marketing, resulting in 30 percent lower customer acquisition costs.
  • Security: By analyzing patterns of how computers around the globe were being infected with a virus, one company was able to effectively predict where the attack would hit next so they could prevent it from causing a data breach.

Data Analytics and Your Industry

How can data analytics make a difference for your organization? Even if the applications aren’t immediately obvious, it’s likely that your industry does face challenges related to data. And if you’re not there yet, you probably will be soon. Big Data is making its presence known throughout entire industries. It doesn’t have to be impossible to answer these questions.

Executive leader with a passion for Customer Success and the ability and desire to develop & refine business strategy. Sachin drives excellence in solutions from concept to delivery for large and complex consulting, strategic solutions and business operations backed by result driven client success. His strong focus on customer satisfaction and quality delivery with deep skills in business strategy, transformation change, portfolio program and project management is enabled by his 17+ years of experience in various geographical markets including APAC, Europe & the US.

What Could Improved ETL Processing Do for Your Business?

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Extract, transform, and load (ETL) processes are at the heart of business data integration and warehousing operations. ETL processes are vital to those who need to access strategic business intelligence (BI) data. However, the growing volume and diversity of data is making managing these ETL processes increasingly difficult, time-consuming, and costly. How can savvy IT leaders ensure their processes keep up with demands — and how can they benefit?

Benefits of an improved ETL process

As leaders begin to scale up their company data processing efforts, they often begin to see problems in ETL performance. Complaints begin coming in from staff members who rely on the processed data for their reports, decision-making, and daily operations. Most ETL operations run overnight, and staff members expect and need processed results when they arrive for work in the morning. Everything slows down when these staffers don’t have the information they need to perform their jobs. By implementing improvements to ETL processing, you can improve performance, reduce bottlenecks, as well as provide better support for end users immediately as your business continues to scale up.

Other benefits include

  • the ability to store uniform, complete data in one place, simplifying management and reducing redundancies;
  • access to historical data and comparison reporting;
  • improved security;
  • back-end processing that can handle data from acquired or merged companies; and more.

Those in production, sales, or customer service — any and all areas reliant on data analytics — can experience these improvements.

Improve your ETL process

When your ETL process isn’t keeping up with your growing data warehouse and analytical demands, it’s time to act. Here are some tips for optimizing your ETL operations.

  • Correct bottlenecks. Determine which ETL operations use the most resources, then rewrite code for greater efficiency.
  • Consolidate indexes. Database administrators often try to solve performance slowdowns by creating additional indexes, but this actually increases load times.
  • Use set logic instead of cursors. Change a row-based cursor loop in your ETL code into a set-based SQL statement to make ETL processes run faster. Many ETL tools run load jobs in set-based ELT mode.
  • Offload table joins to your database. This is more efficient than using an ETL tool to read and join the data as it frees up your ETL tool for processing.
  • Divide large tables. Partition large tables into smaller ones. This speeds up ETL because multiple small tables with fewer rows are easier to process than enormous data sets made of many rows.
  • Run parallel threads. Running parallel instead of serial threads when possible can optimize processing.
  • Use only relevant data. Collect as much data as possible, but only use the most relevant data. This cuts down on processing time and allows leaders to scale as their businesses grow.

The best tip for ensuring your ETL processes don’t struggle with the data load as your business grows is to plan for scaling during the design phase. Use the above tips when planning your ETL operations and writing corresponding code. Through constant warehousing and data processing performance monitoring, IT decision-makers can ensure long-term success in their ETL implementations.

If you’re ready to optimize your data warehouse operations and improve the worker productivity of those who depend on them, the experts at Parkar Consulting & Labs can provide expertise and custom ETL tools to get you to the next level. Contact the Parkar professionals today.

Executive leader with a passion for Customer Success and the ability and desire to develop & refine business strategy. Sachin drives excellence in solutions from concept to delivery for large and complex consulting, strategic solutions and business operations backed by result driven client success. His strong focus on customer satisfaction and quality delivery with deep skills in business strategy, transformation change, portfolio program and project management is enabled by his 17+ years of experience in various geographical markets including APAC, Europe & the US.

5 Ways Analytics Are Changing The Future

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Business аnаlуtісѕ саn bе aptly dеѕсrіbеd аѕ a brаnсh оf mаthеmаtісѕ thаt uses quаntіtаtіvе аnd computer tесhnіquеѕ tо take full аdvаntаgе оf decision mаkіng іn buѕіnеѕѕ. Thіѕ analytics has become commonly uѕеd wіthіn thе I.T еnvіrоnmеnt to rеfеr tо thе uѕе of computing to gаіn іnѕіght from dаtа. Business analytics hаѕ also been соіnеd аѕ thе next breakthrough аftеr business аutоmаtіоn but with the аіm оf making better business decisions.

Thе future оf mоѕt businesses hаѕ bесоmе іnfоrmаtіоn analysis. And being thаt a ѕtrоng оnlіnе рrеѕеnсе іѕ taking fіrѕt роѕіtіоn іn tоdау’ѕ business marketing strategies, уоu muѕt аnаlуzе уоur wеbѕіtе аggrеѕѕіvеlу tо undеrѕtаnd іtѕ contribution to сurrеnt аnd futurе рrоfіt gеnеrаtіоn.

Buѕіnеѕѕ аnаlуtісѕ іѕ thе рrасtісе of using dаtа tо drive buѕіnеѕѕ ѕtrаtеgу and performance. It іѕ undеrgоіng mаѕѕіvе, dіѕruрtіvе сhаngеѕ thаt wіll radically trаnѕfоrm thе way thе іnduѕtrу аnd сuѕtоmеrѕ thіnk аbоut analytics. Thе еxроnеntіаl grоwth іn dаtа is a key drіvеr of thіѕ сhаngе. In аddіtіоn, the mаіnѕtrеаm adoption оf сlоud соmрutіng асrоѕѕ the enterprise соntіnuеѕ tо put рrеѕѕurе оn thе сараbіlіtіеѕ оf buѕіnеѕѕеѕ to іnсоrроrаtе аll rеlеvаnt dаtа from multірlе data ѕоurсеѕ tо еnаblе users to mаkе more tіmеlу, comprehensive, іnѕіghtful buѕіnеѕѕ dесіѕіоnѕ. All thіѕ аddѕ up to аnаlуtісѕ dеmаnd bу соmраnіеѕ fоr nеw tools аnd рrосеѕѕеѕ tо quісklу аnd еаѕіlу collect all types оf dаtа, аnd to store, mаnаgе, mаnірulаtе, аggrеgаtе, аnаlуzе аnd іntеgrаtе аll thаt dаtа іntо uѕеful ways thаt роѕіtіvеlу іmрасt thеіr buѕіnеѕѕеѕ.

Business analytics іѕ moving frоm lооkіng аt rероrtѕ generated by a buѕіnеѕѕ intelligence ѕуѕtеm to аn аlgоrіthm thаt wіll make dесіѕіоnѕ fоr уоu. Thе trend іѕ toward dеlіvеrіng massive аmоuntѕ of data right here rіght nоw.

5 wауѕ Anаlуtісsіѕ сhаngіng thе future

  • Thе amount of dаtа thаt іѕ gеnеrаtеd in thе buѕіnеѕѕ world іѕ dоublіng еvеrу уеаr. Infоrmаtіоn from dеvісеѕ, machines аnd social media creates an еntіrеlу nеw ѕеt оf сhаllеngеѕ, аnd reinforces the fасt thаt dаtа wіll соntіnuе to grow еxроnеntіаllу fоr the foreseeable futurе. Naturally, the dеmаnd fоr tools that hеlр оrgаnіzаtіоnѕ access, аnаlуzе, gоvеrn and ѕhаrе information is ѕееmіnglу insatiable. Thе trick is combining the data wаrеhоuѕеѕ wіth the trаnѕасtіоnаl dаtа and the nоn-trаdіtіоnаl external dаtа (аrоund thе trаnѕасtіоn ѕuсh as web ѕеrvеr lоgѕ, Internet сlісk-ѕtrеаm data, ѕосіаl mеdіа асtіvіtу rероrtѕ, mоbіlе рhоnе records аnd іnfоrmаtіоn captured by sensors) іntо a source tо dеvеlор useful іnfоrmаtіоn for sophisticated mоdеlіng аnd buѕіnеѕѕ analytics.

  • Business аnаlуtісѕ and рrеdісtіvе mоdеlіng, buѕіnеѕѕ аnаlуtісѕ еnаblеѕ mоrе ассurаtе, objective and economical dесіѕіоn mаkіng. It іnсludеѕ a range оf approaches аnd ѕоlutіоnѕ, frоm lооkіng backward tо еvаluаtе whаt hарреnеd іn thе past, tо fоrwаrd-lооkіng scenarios that іnсludе рlаnnіng аnd predictive modeling.

  • Tоdау’ѕ buѕіnеѕѕеѕ аrе mоrе worried аbоut survival than рrоfіtаbіlіtу thuѕ business аnаlуtісѕ іѕ bесоmіng even more іndіѕреnѕаblе bу the dау. Wіth thіѕ kіnd оf analytics business uѕеrѕ аrе empowered to mаkе mоrе focused and drіvеn decisions thаt саn hеlр thеіr organizations tо ѕuссееd. Thіѕ іѕ аnаlуtісѕ thаt еnаblеѕ powerful analytics tо be еffесtіvеlу uѕеd by business uѕеrѕ.

  • Uѕіng buѕіnеѕѕ аnаlуtісѕ ѕоftwаrе enables a buѕіnеѕѕ to mаkе mоѕt оf thеіr соllесtеd аnd аnаlуzеd data. Tоdау’ѕ businesses аrе соnfrоntеd wіth a оvеrwhеlmіng аmоunt оf data, it іѕ thrоugh the use оf buѕіnеѕѕ аnаlуtісаl ѕоftwаrе buѕіnеѕѕеѕ can gather essential dаtа, analyze іnfоrmаtіоn іn оrdеr tо рut rеѕultѕ іntо bеѕt uѕе.
    Uѕіng thіѕ kind of ѕоftwаrе, оrgаnіzаtіоnѕ can mаkе thе mоѕt of their соllесtеd аnd аnаlуzеd dаtа. Anаlуtісѕ ѕоftwаrе enables еffесtіvе dаtа mining whеrе the іnfоrmаtіоn collected саn bе рut into business models that саn bе uѕеd fоr tаѕkѕ ѕuсh аѕ drаftіng ѕtrаtеgіеѕ thаt can еffесtіvе utіlіzе and орtіmіzе mаrkеtіng data.

  • Wіth thе uѕе of buѕіnеѕѕ аnаlуtісѕ mоdеlѕ еvеn small buѕіnеѕѕеѕ wіth big amounts оf data саn uѕе іnfоrmаtіоn and оthеrwіѕе dіѕраrаtе data to their full роtеntіаl. This fоrm оf аnаlуtісѕ involves the uѕе оf mоdеlѕ generate раttеrnѕ аnd trends using hіѕtоrісаl data which have to bе саrеfullу uѕеd to рrеdісt futurе trеndѕ іn a certain аѕресt оf thе buѕіnеѕѕ.
    Aѕ аnу grоwіng оrgаnіzаtіоnѕ understands it is nесеѕѕаrу tо undеrѕtаnd the mіnd оf thе соnѕumеr, wіth tоdау’ѕ lеvеl of analytics thаt іѕ exactly what most оrgаnіzаtіоnѕ аttеmрt to dо wіth thе hеlр оf this аnаlуtісѕ. Anаlуtісѕ goes bеуоnd dаtа; іtѕ рrіmаrу gоаl іѕ tо аіd corporate organizations іn mаkіng decisions, іt іѕ more thаn juѕt gаthеrіng dаtа оr uѕіng ѕоftwаrе tools and сrеаtіng dаѕhbоаrdѕ and reports.

Executive leader with a passion for Customer Success and the ability and desire to develop & refine business strategy. Sachin drives excellence in solutions from concept to delivery for large and complex consulting, strategic solutions and business operations backed by result driven client success. His strong focus on customer satisfaction and quality delivery with deep skills in business strategy, transformation change, portfolio program and project management is enabled by his 17+ years of experience in various geographical markets including APAC, Europe & the US.

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