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Introduction to Challenges of Big Data Analytics

Data is a very valuable asset in the world today. The economics of data is based on the idea that data value can be extracted through analytics. Though Big data and analytics are still in their initial growth stage, their importance cannot be undervalued. As big data starts to expand and grow, the Importance of big data analytics will continue to grow in everyday personal and business lives. In addition, the size and volume of data are increasing daily, making it important to address big data daily. Here we will discuss the Challenges of Big Data Analytics.

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According to surveys, many companies are opening up to using big data analytics in their daily functioning. With the rising popularity of Big data analytics, it is obvious that investing in this medium will secure the future growth of companies and brands.

The key to data value creation is Big Data Analytics, so it is important to focus on that aspect of analytics. Many companies use different methods to employ Big Data analytics, and there is no magic solution to successfully implementing this. While data is important, even more important is the process through which companies can gain insights with their help. Gaining insights from data is the goal of big data analytics, so investing in a system that can deliver those insights is extremely crucial and important. Therefore, successful implementation of big data analytics requires a combination of skills, people, and processes that can work in perfect synchronization with each other.

With great potential and opportunities, however, come great challenges and hurdles. This means that companies must be able to solve all the hurdles to unlock the full potential of big data analytics and its concerned fields. When big data analytics challenges are addressed in a proper manner, the success rate of implementing big data solutions automatically increases. As big data makes its way into companies and brands around the world, addressing these challenges is extremely important.

Major Challenges of Big Data Analytics

Some of the major challenges that big data analytics programs are facing today include the following:

Uncertainty of Data Management Landscape: Because big data is continuously expanding, new companies and technologies are developed every day. A big challenge for companies is to find out which technology works bests for them without introducing new risks and problems.

The Big Data Talent Gap: While Big Data is growing, very few experts are available. This is because Big data is a complex field, and people who understand this field’s complexity and intricate nature are far from between. Another major challenge in the field is the talent gap that exists in the industry

Getting data into the big data platform: Data is increasing every single day. This means that companies have to tackle a limitless amount of data on a regular basis. The scale and variety of data available today can overwhelm any data practitioner, which is why it is important to make data accessibility simple and convenient for brand managers and owners.

Need for synchronization across data sources: As data sets become more diverse, they must be incorporated into an analytical platform. It can create gaps and lead to wrong insights and messages if ignored.

Getting important insights through the use of Big data analytics: It is important that companies gain proper insights from big data analytics, and it is important that the correct department has access to this information. A major challenge in big data analytics is bridging this gap in an effective fashion.

This article will look at these challenges in a closer manner and understand how companies can tackle these challenges in an effective fashion. Implementation of Hadoop infrastructure. Learn Hadoop skills like HBase, Hive, Pig, and Mahout.

Challenge 1

The challenge of rising uncertainty in data management: In a world of big data, the more data you have, the easier it is to gain insights from them. However, in big data, there are a number of disruptive technology in the world today, and choosing from them might be a tough task. That is why big data systems need to support both the operational and, to a great extent, analytical processing needs of a company. These approaches are generally lumped into the NoSQL framework category, which differs from the conventional relational database management system.

There is a number of different NoSQL approaches available in the company, from using methods like hierarchal object representation to graph databases that can maintain interconnected relationships between different objects. As big data is still in its evolution stage, there are many companies that are developing new techniques and methods in the field of big data analytics.

In fact, new models developed within each NoSQL category help companies reach their goals. These Big analytics tools are suited for different purposes as some provide flexibility while other heal companies reach their goals of scalability or a wider range of functionality. This means that the wide and expanding range of NoSQL tools has made it difficult for brand owners to choose the right solution to help them achieve their goals and be integrated into their objectives.

Challenge 2

The gap in experts in big data analytics: An industry completely depends on the resources it has access to, whether human or material. Some tools for big data analytics range from traditional relational database tools with alternative data layouts designed to increase access speed while decreasing the storage footprint, in-memory analytics, NoSQL data management frameworks, and the broad Hadoop ecosystem. With so many systems and frameworks, there is a growing and immediate need for application developers who have knowledge of all these systems.  Despite the fact that these technologies are developing at a rapid pace, there is a lack of people who possess the required technical skill.

Another thing to remember is that many experts in the field of big data have gained experience through tool implementation and its use as a programming model instead of data management aspects. This means that many data tool experts lack knowledge about the practical aspects of data modeling, data architecture, and data integration.

This lack of knowledge will result in less than successful data and analytical processes implementations within a company/brand.

According to analyst firm McKinsey & Company, “By 2023, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effective decisions.

All this means that while this sector will have multiple job openings, there will be very few experts who will actually have the knowledge to fill these positions effectively. While data practitioners become more experienced through continuous working in the field, the talent gap will eventually close. At the same time, it is important to remember that when developers cannot address fundamental data architecture and data management challenges, the ability to take a company to the next level of growth is severely affected. This means that companies must always invest in the right resources, be it technology or expertise, to ensure that their goals and objectives are objectively met in a sustained manner.

Challenge 3

As companies have a lot of data, understanding that data is very important because, without that basic knowledge, it is difficult to integrate it with the business data analytics program. Communication plays an integral role here as it helps companies and the concerned team educate, inform and explain the various aspects of business development analytics.

Before even going towards implementation, companies must a good amount of time explaining the benefits and features of business analytics to individuals within the organizations, including stakeholders, management, and IT teams. While companies will be skeptical about implementing business analytics and big data within the organization, once they understand its immense potential, they will easily be more open and adaptable to the entire big data analytical process.

Challenge 4

The challenge of the need for synchronization across data sources: Once data is integrated into a big platform, data copies are migrated from different sources at different rates. Schedules can sometimes be out of sync within the entire system. There are different types of synchrony. It is important that data is in sync. Otherwise, this can impact the entire process.  With so many conventional data marks and data warehouses, sequences of data extractions, transformations, and migrations, there is always a risk of data being unsynchronized.

With exploding data volumes and the rising speed at which updates are created, ensuring that data is synchronized at all levels is difficult but necessary. This is because data is not in sync. It can result in analyses that are wrong and invalid. If inconsistent data is produced at any stage, it can result in inconsistencies at all stages and have disastrous results. Wrong insights can damage a company to a great degree, sometimes even more than not having the required data insights.

Challenge 5

The challenge of getting important insights through Big data analytics: Data is valuable only as long as companies can gain insights from them. By augmenting the existing data storage and providing access to end users, big data analytics needs to be comprehensive and insightful. The data tools must help companies not just have access to the required information but also eliminate the need for custom coding. As data grows inside, it is important that companies understand this need and process it in an effective manner. With the increase in data size on time and cycle, ensuring the proper adaptation of data is a critical factor in the success of any company.


These are just some of the few challenges that companies are facing in the process of implementing big data analytics solutions. While these challenges might seem big, it is important to address them in an effective manner because everyone knows that business analytics can truly change the fortune of a company. The possibilities with business analytics are endless, from preventing fraud to gaining a competitive edge over competitors to helping retain more customers and anticipating business demands. In the last decade, big data has come a very long way, and overcoming these challenges is going to be one of the major goals of the Big data analytics industry in the coming years.

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Process And Challenges Of Data Governance

Introduction to Data Governance

Data governance can be defined as a compilation of various activities involved for utilizing the data for facilitating the organization towards its purpose. These activities include the organization’s practices, dedicated roles for every activity, regulatory policies & terms to be followed, etc. When these activities are kept in – check, the data Governance is said to be exceptional by attaining maximum efficiency for data processing. This process helps in commencing the data management techniques and related tasks so as to guarantee the performance and data security for the data being handled transversely through the organization, which can be applied at any time and in any situation.

Components of Data Governance

Hadoop, Data Science, Statistics & others

Below are the categories with respect to the said questions:

WHO – People in the Organization

User Group Charter

Escalation Hierarchy

Role Structure

Data Keepers

Decision Making Professionals

WHY – Target

Achieve the set Goals

Mission & Vision

WHAT – Centre of Attention

Factors that Quantify

Financial Support

Regulatory Plan

Data Management Methods

WHEN – Activities

Data Analysis

Process Plan

Implementation of Plan

Data Management Process

Record the Process Progress

When these components work along with one another, there comes efficient Data Governance for the given organization. Under each of these components, there can be a number of people, roles, and actions carried out. Apart from this, Data Governance can be achieved technologically with the help of the Metadata Repository, Data Profiling Tools, Data Cleansing Tools, Data Mining Tools, Data Management activities, etc.

A mixture of processes performed in Data Governance is sticking to the organization’s policies, process monitoring, data cleansing, data profiling, data extraction, data analysis, data processing, etc. These smaller processes make a bigger impact on the organization’s Data Governance rates, as each of the activities is interdependent in most of the cases.

Data Governance Process

A well-controlled Data Governance approach is an essential constituent for an institute’s performance, where the data science technologies are put in use. It aids in taking the multiple Data Governance components in –line, which simply means all the minor activities that contribute to any and all the Data Governance components. The people/ roles involved in this process, like Business professionals, who make the important decisions, are responsible for emphasizing the particulars on the data to be handled under data governance.

Data Governance Challenges

Constructing and applying a Data Governance flow in an institution is an intimidating and overpowering task. In addition to a proper management team, Data Governance requires a completely workable plan and the ability to follow the plan to the point, without any deviations. A few of the common problems observed in employing the Data Governance technique in an organization are:

1. Roles and Responsibility

It is hard to place the respective people’s inappropriate roles for handling the designated responsibilities in the data sector in the said organization. Structuring the data governance procedure involves people handling, placing the respective person in a suitable role, access level to the data, every person’s accountability, etc. On the highest level is the Chief Data Officer of the organization, which has become a regular practice in this technologically evolving world of Data.

2. Data Handling

A Major hiccup observed in many cases of Data Governance application is that the data is gathered from multiple data sources, and not all the data sources are going to be clean and processed. It is important to gather junk-free data in order to maintain the efficiency of the Data Governance method produced by the organization.

3. Data Store/ Data Mart 4. Organization’s Preparedness

When organizations pick up the benefits of Data Governance and decide to implement the same in their own organization, it is highly likely that they look only at the external factors. It is equally important to look at the internal practices of the organization that are being practiced in the current situation. The work pattern should be modified in a way to accommodate the upcoming changes along with the Data Governance execution. While the Data Governance planning is in progress, the existing tradition of the institute should be rechecked and keep the platforms open to place the Data Governance production in place.


Taking up the Data Governance method into an organization can improvise the company’s Data Management ability while holding the quality and value of the in–house data.

Data grouping from different areas and sectors can help all future processes.

Imposing people’s positions and responsibilities in data administration.

Allowing peripheral data contribution assignments.

Sustaining rigid consistency agreements.

Modeling the data regulation and consumption of the data operated in Data Governance.

Helps in trimming down the disorganized data and managing the costs for data management.


To sum up, Data governance assists in making definite about the places of every role and task that is required for carrying out of Data Governance in a Venture. When designing the Data Governance configuration by embracing the deliberations, the company’s imprudence is fixed once for all in terms of administration and management of data.

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Big Data / Analytics Based Startups At Y Combinator, Summer 2023 Batch

If there is one startup accelerator, the tech world keeps a watch on – it is Y Combinator! The accelerator has produced the likes of Reddit, Dropbox & Airbnb in their 10 years of existence. Here is what some of the best brains in the industry say about it:

“Several of our best investments have come from Y Combinator. Y Combinator is the best program for creating top-end entrepreneurs that has ever existed.” —Marc Andreessen, General Partner, Andreessen Horowitz

On August 21, 2023 the twentieth batch of Y Combinator went through their demo day! Here is a brief introduction to some of the best big data & analytics based startups, which are in store.

Also read: “Big data/ Analytics startups of 2023 winter batch”

Analytics / Big Data startups in Y Combinator Summer 2023 batch:

Credit card companies can mine ton of information from transactions of its customers. Imagine what you can do by inspecting billions of credit card transactions? Second Measure delivers insightful and effective analysis by inspecting billions of credit card transactions and presenting detailed insights to investors and analysts through their products. Second Measure can deliver various insights like:

Visibility into all private and public companies those sell directly to US consumers

Benchmark the rank of companies against their competitors

Provide insights on customer retention, engagement and life time value of customers of the company

Verge Genomics uses genomic data analysis to find better drugs for brain diseases. Cause of Neurological diseases are complex interactions between many genes. Most of the drugs fail because researchers target only one gene at a time but Verge Genomics discovered a way to map out the hundreds of genes that cause a disease, and then find drugs that target all the genes at once. Some of the benefits of this method are:

Cures disease 1000X more cost effective and quicker compare to tradition way.

More recovery of patients

How do you provide credit in a market where credit history is not available? Greenshoe is solving this problem in Africa by using smartphone data. It is a lending platform for emerging market (starting in Africa) that helps people get approved for short-term loans based on their activity on their phones. To apply for a loan, you have to follow the following process:

Sign-up with your phone number, name and identification number

Share data of your smartphone like how often you recharge, what proportion of the plan is spent on data services, voice or text messaging” along with optionally answer a few questions

They’ll analyse and tell you how much money you can request for along with the loan terms

Send the money to your Mobile Wallet account in minutes

Repay your Loan in installments or all at once before the due date. No penalties for earlier payment

Remember the joke “You can never find an average man?” Ixchel Scientific is applying this simple logic to make cancer treatment far more effective. It is a Biotech startup, which works on improving the success rates for trialed cancer drugs using an environment that better represents the human body. Currently 95% of cancer drugs fail human testing after investing billions of dollars and decades inventing in development. Ixchel Scientific provides organ specific platform that predicts how drug will behave in the human body. They offers various services like correlation analysis with clinical responses, Drug candidate screening, fully customizable, Rescue failed drug candidates and others.

SourceDNA has built a stockpile of the most detailed data on how developers use tools, SDKs and plugins to create the top-ranked iOS and Android apps, paid and free, in 20 countries. Their customers harness the intelligence to spot new competitors and identify new developer trends that surprise their product and sales teams. They continuously update the data and keep it astonishingly deep, keeping their customers uncannily ahead of the mobile market.

GovPredict quantifies, tracks, and predicts legislative activity. With big data and powerful algorithms, they predict the likeliest cosponsors for legislation and convince them before competitors could reach out to them. Discover unlikely voting and cosponsorship alliances for specific kinds of bills, by keyword, originating committee, or sponsor party!

Launch a Private Cloud in Minutes and it come up with the simplicity of Heroku and the power of AWS. It uses ECS, ELB, Kinesis, and many other great AWS services under the hood but automates it all away to give you a deployment experience even easier than Heroku. It has various features like Instant Deployment, Standardize Development and it runs in your AWS account.

Urban construction and development is fraught with uncertainties and one of the far off places for analytics. However, Vernox is applying artificial intelligence techniques to the pile of e-mails, Word documents and Excel files that are exchanged between contractors and designers. From that data, Vernox labs produces automated design reviews that project managers can run through with a new proposal. They are also creating a Google like search engine which can be used to query for specific materials or components.

So, you have been working out for 3 months now but you are not seeing the effect you would have liked to? Shape scale not only monitors basic things like changes in weight but also changes at multiple body parts over time. Shoulder growth of 10% over a month, fat gain of 5%, belly diameter growth of 1% are some of the results delivered by ShapeScale. It is your own body monitor. Using predictive algorithms on previous work out data, shape scale helps you get the body of your dreams.

Unmanned aerial vehicles are a reality nowadays and here comes in Flirtey with it’s delivery by drones system. You place an order and a drone drops it at your home. Scary? Well, it’s convenient, no delays due to traffic or any other human intervention. Flirtey has partnered with the University of Nevada to deliver on it’s promise and is currently operating in New Zealand. With time it is going be an industry and not a thing of the future anymore.

Imagine having the law at your fingertips. Imagine having a Google like service where you can query: ”When can a debtor reject a collective bargaining agreement?” and pop comes some relevant links that give you the answers you were looking for. Built on top of IBM’s Watson, Ross sifts through all the law literature and returns only relevant data. It also monitors the law to inform you about relevant changes to the law which might affect your case. This is the perfect synthesis of law and technology for the greater good.

With GrowSumo, a company can manage all it’s partners from a single page and add new partners, train them, reward them and also track their progress. Re-sellers can find products that they know make their clients happy. You can also work with companies your client already loves and discover new services from that which you think your client might like. The more recommendations you make the more money you can make. For example an educational institute listed on GrowSumo can be an attractive options to an online education provider for any particular course. They can apply to the educational institution and get trained. If it works out well each can recommend the other to businesses they know would benefit. This is thus an online community of companies.

A smart mattress cover that understands how you sleep and monitors your heart rate etc. while you are sleeping. Sleep is one of the most important functions that the body has to go through and we all know how groggy the next morning can be in absence of a good nights sleep. Just plug in Luna to a power source and let it handle this problem for you. It learns from your sleeping patterns and adjusts the bed temperature to your liking. It wakes you up in the moment of lightest sleep so that you feel refreshed when you wake up. Plug it in, connect to wi-fi and turn on the luna app. Sleep – Well.

It is a IoT venture from the Hravard Innovation Lab that creates products (software and hardware) which help engineers and scientists to monitor their machines, experiments on the cloud. An engineer can fit sensors to pressure gauges and monitor them on the cloud. A scientist can monitor his lab or experiments without being present there through a camera built by Tetra. So,buy a tetra science product, fit it to your existing machines and log into your tetra science account to monitor, manage and analyze data using your computer or cell phone, IoT is here.

Hickory provides continuous learning through personalized learning material to increase employee knowledge and application. You learn and the Hickory algorithm adjusts the material based on your mastery over the subject. So learn a material, get a review done by Hickory and the next material is based on how much you have learned. A personalized teacher at your service.

It creates your personalized Digital Pantry to keep track of ingredients in your kitchen, so you always receive just what you need by asking innovative Personalized Quiz to understand your preferences, time constraints, and goals. It uses Waste Reduction Algorithm to utilize ingredients across recipes to minimize food waste while optimizing for ingredient freshness and variety.

PickTrace is developed as a response to farm’s needs for a harvesting management. Today, it has grown into a suite of tools to perform entire operations like Real Time Analytics, Labor Management, Harvest Management and Legal Compilancce. It has a reliable, hassle-free solution, which helps farmers to improve productivity.

While Team Leada is not using machine learning or Big Data to solve a problem, they are teaching Big Data and Data Analysis in a unique blend of case studies, class room and support for coding. Professors can use them to supplement their lectures and Enterprises can use them to train the employees. While you visit their site, the team has also created four awesome books on data science after talking to thought leaders in the industry – you should check them out as well.

End Note:

So, there you see, Big Data and Data Science are touching every part of your life, be it learning, sleeping, harvesting to shopping! An exciting world and time to live in. All of these ideas and startups are exciting and on their track to change traditional industries fundamentally or create new ones – and all of this is based on analytics and big data!

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.


Combination Of Virtual Reality And Data Analytics

Virtual reality is an innovation with boundless opportunities. These can be seen when it is combined with another tech to make new opportunities. At the point when paired with gaming, for instance, VR has empowered the user to enter the virtual universe of the game, for example, in an online casino where the user can enter a virtual casino from the comfort of their own home. When utilized in marketing, property developers can demonstrate houses to potential buyers any place they were on the planet. We are living in an exceptional time. Information technology is changing basically every part of present-day society: how we work, play, learn and talk. The pace of the change is remarkable with the significant changes occurring on the size of years instead of decades or hundreds of years. One result of this unrest is an exponential growth of data rates and data volumes, mirroring Moore’s law that depicts the rapidly evolving technology that produces the data. Just as significant is the growth of information quality and data multifaceted nature. Yet, imagine a scenario in which there was an approach to visualize huge data sets that in a flash uncovered significant patterns and trends. Consider the possibility that you would associate with the information, move it around, truly stroll around it. That is one of the lesser talked about promises of mixed reality. If engineers can deliver on the promise, it just might be one of the most significant enterprise applications of those rising innovations, also. Despite the fact that it’s initial days, augmented reality and virtual reality could on a very basic level change the manner in which we interact with and decipher the information. Going ahead the impact points of the big data revolution, 3D visualizations in mixed reality are the correct tool at the ideal time to help decision-makers comprehend and gather insights from huge data sets. The innovation will open the intensity of big data in realms as unique as community health & medicine, agriculture, board rooms, and governments, and it could hasten the adoption of enterprise AR/VR, which has had a rough gathering up until now. Things being what they are, a perception of the patterns and relations present in the data works much better when we are completely submerged in such a data space, taking a look at the data from the back to front as opposed to from the outside glancing in, just like the case in all traditional visualization approaches. This impact has been shown by various research studies in different domains. We are animals improved to manage the physical 3D world in which we are submerged, and our brains are best if we are looking for patterns in such a space regardless of whether space itself is abstract in nature. The next big application of VR technology will be consolidating it with Big Data to tackle the issue made by the constraint of human perception. The tremendous amount of data which is accumulated through user interaction is an amazingly incredible asset if it can be sorted into useful information. Sorting this data is essential to settle on informed decisions in the competitive condition regarding online businesses. Conventional visual diagrams and pie outlines on 2D screens are not slicing it with regards to processing large data sets. VR hence gives an elective method for reviewing information by utilizing its immersive abilities to take care of complex issues. This idea is alluded to as data visualisation which includes making an immersive experience where the information models encompass you.

Data Analytics Vs. Data Science

Data analytics and data science are closely related technologies, yet significant differences exist between them.

Data science, in contrast, focuses on the larger picture of data, and involves creating new models and systems to build an overall portrait of a given data universe.

In essence, data science takes a “larger view” than data analytics. But both data methodologies involve interacting with big data repositories to gain important insights.

For more information, also see: What is Big Data Analysis

Data Science Data Analytics

Scope Macro Micro


ML software development

Predictive analytics

Engineering and programming

BI tools

Statistical analysis

Data mining

Data modeling

Goal To extract knowledge insights from data To gain insights and make decisions based on data

Popular Tools Python, ML, Tableau, SQL SQL, Excel, Tableau

As noted, while data analytics and data science and are closely related, they both perform separate tasks. Some more detail:

Data analytics analyzes defined data sets to give actionable insights for a company’s business decisions. The process extracts, organizes, and analyzes data to transform raw data into actionable information. Once the data is analyzed, professionals can find suggestions and recommendations for a company’s next steps.

Data analytics is a form of business intelligence that helps companies remain competitive in today’s data-driven market sectors.

For more on data analytics: Best Data Analysis Methods

Data science is the process of assembling data stores, conceptualizing data frameworks, and building all-encompassing models to drive the deep analysis of data.

Data science uses technologies that include statistics, machine learning, and artificial intelligence to build models from huge data sets. It helps businesses answer deeper questions about trends and data flow, often allowing a company to make business forecasts with the results.

Given the complexity of data science, it’s no surprise that the technology and tools that drive this process are constantly – and rapidly – evolving, as they are with data analytics.

For more on data science: Data Science Market Trends

Both data analytics and data science are essential disciplines for companies seeking to find maximum benefit from their data repositories. Among the benefits:

Streamline operations: Data analytics has the potential to gather and analyze a company’s data to find where current production is slowing and improve efficiency by helping a company predict future delays.

Mitigate risks: Data analytics can help companies see and understand their risks. Data analytics can help take preventative measures as well.

Discover unknown patterns: Data science can find overall patterns within a company’s collection of data that can potentially benefit them. Analyzing these larger, systemic models can help a business understand their workflow better, which can support major business changes.

Company innovation: With data science, a company can find foundational problems that they previously did not fully realize. This deep insight benefits may benefit the company at several different levels of operation.

Real-time optimization: The larger vision offered by data science enables businesses to react to change quickly –  an overall systemic view offers great guidance.

For more information: Data Science & Analytics Predictions, Trends, & Forecasts

Lack of communication within teams: Team members and executives may not have the expertise to provide much granular insight into their data, despite their control over it. Without a data analyst, a company could miss information from different teams.

Low quality of data: Decisions for a company can be negatively affected if low-quality data or data that has not been fully prepped is involved in the process.

Privacy concerns: Similar to data science, there are problems with privacy while using data analytics. If a company or professional does not govern sensitive information in a compliant manner, the data can be compromised.

Domain knowledge required: Using data science requires a company or staffer to have significant knowledge about data science as it grows and changes, which means that companies must allot budget for hiring and training qualified professionals.

Unexpected results: Occasionally, data science processes cannot incorporate or mine data that is considered “arbitrary” data, meaning data this is not recognized by the system for any reason. Because a data scientist may not know which data is recognized, data problems could go under the radar.

Data privacy: As with data analytics, if data is treated without careful standards, the large datasets are more susceptible to cybersecurity privacy problems.

Companies need to select the optimum tools to use data analytics and data science most  effectively. See below for examples of some leading tools:

Here are the top six data analytics tools and what they can do for a business:

Tableau: Collects and combines multiple data inputs and offers a dashboard display with visual data mining.

Microsoft Power BI: AI and ML functionality, powering the augmented analytics, and image analytics.

Qlik: AI and ML, easy deep data skills, and data mining.

ThoughtSpot: Search-based query interface, augmented analytics, and comparative analysis to anomaly detection.

Sisense: Cloud-native infrastructure, great scalability, container technology, caching engine, and augmented data prep features.

TIBCO: Streaming analytics, data mining, augmented analytics, and natural language user interface.

Here are the top six data science tools and what they can do for a business:

When researching which data analytics and data sciences tools to buy, it is important to understand that data analytics and data science work in combination with one another – meaning that more than one software tool may be needed to create the optimum data strategy.

In some cases this means buying both data solutions from one vendor, but this isn’t necessary. It also works to buy “best of breed” from two different – competing – vendors. Just make sure to do an extensive trial run with both applications working in concert, to ensure that the combination creates the ideal result.

Data science and data analytics are separate disciplines but are both are crucially important to businesses.

For businesses looking to increase their understanding of data and how it can help their organizations, data analytics and data science play a contrasting and complimentary role. They are different – but they are both essential.

Therefore, business must understand the differing roles of data analytics and data science, and be prepared to select tools for each discipline that work well in combination.

Mining For Big Value In Big Data

Nate Silver knows a thing or two about the value of Big Data. He famously predicted who would win 49 out of 50 states in the 2008 Presidential election and the following year was named by Time one of The World’s 100 Most Influential People. Silver’s Five Thirty Eight site focuses on “data journalism.”

But Silver warned in a recent keynote at the Rich Data Summit that Big Data can be misunderstood and oversold. A popular notion, Silver said, is that “you get your data, you press a button and all of a sudden you have extremely valuable output. This idea is very wrong and dangerous.”

In fact, the work data scientists do is far more complex. “Data scientists aren’t interested in data for data’s sake, we’re interested in relationships,” he said.

Jenny Dearborn, author of the book Data Driven: How Performance Analytics Delivers Extraordinary Sales Results, says we are a tipping point in our ability to collect, manipulate, analyze and act on big data.

“We finally have the ability to manipulate all this data we’ve been collecting and understand what to do with it,” says Dearborn, Chief Learning Officer at SAP. “We certainly had the information before, but it was hard to access and compile and get a big picture view of it; it was very much nose to the tree. Now we can see the forest and patterns and trends and ‘what does all this mean?’

“With all this information we can for the first time really answer some very big strategic questions: ‘What is the business problem we’re trying to solve?’  ‘What are the big trends here’?’” says Dearborn.

But she agrees with Silver there is no magic button to realizing the benefits of Big Data. 

“It’s challenging, because it’s not just having the data, but knowing what to do with it,” says Dearborn. “Knowing what questions to ask, what business problems you are trying to solve and how do you apply analytics to the data you have to answer those big questions. There’s a lot of big strategic thinking that needs to happen in front of looking at your stacks of data or all your reports, and sometimes companies don’t take the time to ask those big questions. “

Discovering New Flavors

Some Big Data insights are relatively straightforward. Coca Cola has leveraged results from its network of Freestyle drink dispensers to create a new flavor. Freestyle, a kind of drink factory in a touchscreen box, lets customer mix and match over 170 brands of beverages at fast-food outlets, movie theaters and elsewhere. The soft drink giant is able to collect and analyze all those choices. When it saw a pattern of customers mixing Cherry and Vanilla Coke flavors, voila, it created a new, instantly popular flavor, Coke Cherry Vanilla.

Analyst Doug Henschen at Constellation Research points to manufacturing companies like GE and John Deere who are using Big Data to anticipate when parts are going to need to be fixed, resulting in savings on inventory and maintenance costs.

Analyst Bob O’Donnell agrees and adds that even with the right structure and investment Big Data may fall short of expectations.

O’Donnell also says some companies aren’t prepared to leverage Big Data results. A Big Data analysis may help Company X find, for example, that its product doesn’t appeal to single women over 40, but there may be no support internally at the company to change the product or strategy to address that market.

Big Data Rules of the Road

Andreas Weigend, the former Chief Scientist at chúng tôi who now runs Social Data Lab, shared some rules of the road at Rich Data Summit when it comes to starting a Big Data project.

1) Start with the problem, not with the data. If you start with data it grows exponentially and it will be a hose you can’t clean fast enough. Be clear about what question or problem you are trying to solve.

2) Be wary of consultants who say ‘Give us all your data and we’ll give you insights.’ Focus on decisions and actions you can take yourself.   

3) Use metrics that matter to your customers. If you’re in a business that ships products to consumers, it may seem great to find out they’re arriving a day ahead of schedule. But actually that’s a hassle for the customer who planned to be home a day later to receive the package and finds an ’undeliverable’ note on their door.

4) Let people do what people are good at, and computers do what computers are good at.

5) Don’t blame technology for problems that you have in your institution. Weigand uses the example of not being allowed to use third party software when he was teaching at Stanford. “I got a note for using LinkedIn in one of my courses,” he recalled. “You wonder what planet this person is living on.”

Big Data and the Cloud

Analyst Charles King at Pund-IT says the growth of open source frameworks for handling Big Data sets like Hadoop and Apache Spark have led to more companies experimenting with and embracing Big Data.

“You can put together a Hadoop system relatively cheaper, though there’s a lot of assembly and technical expertise required,” says King. “Or you can have a third party like HortonWorks do it.”

He notes that operating a Big Data platform typically requires trained data scientists, who are in relatively short supply.  King also expects to see more cloud-based Big Data projects that require a minimum of on-premise infrastructure. “Certain types of one-off projects could run only 1-6 months,” he said. “As Big Data matures, I think in the short term we’re going to see a growing number of companies offer Big Data as a service with the cloud as the backend.”

Big Data is also entering new areas such as physical store locations. A company called RetailNext helps big retailers do Big Data analysis in part by analyzing video feeds of how customers act in retail locations, e.g. what displays they gravitate to or ignore.

“If you look at chúng tôi chúng tôi or any ecommerce site, they have so much data and they use analytics to constantly improve the way they run their websites,” Alexei Agratchev, CEO of RetailNext, recently told The San Jose Mercury News. “Then you walk through Nordstrom or Victoria’s Secret and nobody has any idea what happens.”

Weigand says whatever the Big Data project you embark on, keep an eye on how it’s going.

 “Does your product or service get better or worse with a Big Data project over time? I think we all know examples from both.”

Photo courtesy of Shutterstock.

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