Kai Goerlich is Research Director, IoT, Supplier Networks, and Digital Futures for SAP

1. McKinsey Global Institute, The Internet of Things: Mapping the Value Beyond the Hype (McKinsey & Company, June 2015), https://www.mckinsey.de/sites/mck_files/files/unlocking_the_potential_of_the_internet_of_things_full_report.pdf
2. “A Guide to the Internet of Things,” Intel Corp., accessed April 14, 2016, http://www.intel.com/content/dam/www/public/us/en/images/iot/guide-to-iot-infographic.png
3. SPB Global, A Trillion Sensors Is the Equivalent of 150 Sensors per Human on Earth. September 26, 2014 http://spb-global.com/uncategorized/trillion-sensors-equivalent-150-sensors-per-human-earth/
4. Julio Bezerra, Wolfgang Bock, François Candelon, Steven Chai, Ethan Choi, John Corwin, Sebastian Digrande, et al. The Mobile Revolution: How Mobile Technologies Drive a Trillion-Dollar Impact (The Boston Consulting Group, January 2015), ch. 2, https://www.bcgperspectives.com/Images/The_Mobile_Revolution_Jan_2015_tcm80-180510.pdf
5. Cisco, Global Cloud Index (GCI), 2015 http://www.cisco.com/c/en/us/solutions/service-provider/global-cloud-index-gci/index.html
6. IDC, The Internet of Things and Digital Transformation: A Tale of Four Industries, 2016, http://www.digitalistmag.com/executive-research/internet-of-things-and-digital-transformation-tale-of-4-industries
7. IDC, The Internet of Things and Digital Transformation: A Tale of Four Industries
8. SPB Global, A Trillion Sensors Is the Equivalent of 150 Sensors per Human on Earth

The Importance of the Internet of Things : Live Business

The Internet of Things (IoT) is not merely a step along the path to digital transformation; it is the driving force.

The real value of the IoT doesn’t come from all the connections it creates but from the data it generates. With real-time data analytics, the IoT becomes a live communications network for fostering insights and improvements. It will also become the foundation of Live Business, in which companies will be able to sense and respond to customers in the moment.

Sensors are the messengers of the IoT, and by 2030 there will 1 trillion of them.³ By putting sensors on everything around us – even inside of us – we will be able to exchange information and participate in a global data network. The IoT will become ubiquitous.

New Interactions Between People, Things, and Machines

We will see many new interactions between people, things, and machines that today seem like science fiction (see Figure 1). Many of the things in our daily lives at home and work will interact with each other, enabling us to use them in new ways. In this connected world, having access to things like cars will become more important than owning them.

At the human-to-machine level, we will be able to gauge the status of machines, receive warnings when they need maintenance, and control their roles in production at any given moment. The IoT will also enable us to control robotic extensions of our bodies, such as replacements for lost or disabled limbs or suits to improve our strength and other capabilities. And humans and machines will work together as teams, communicating through the IoT.

Using the IoT, machines will coordinate and communicate with other machines to create large armies of automated bots capable of acting together in swarms, as ants do in nature. Machines will also be able to monitor one another for potential problems and perform repairs without human intervention.

While mobile made people a part of the digital revolution, the IoT enables everything else to be part of that transformation. Because the IoT will affect so many different aspects of the economy, its generated value will exceed that of mobile.

Mobile started in 1990 with 2G, got more serious in 2000 with 3G, and finally kick-started the smartphone with 4G in 2010. With each technical leap, mobile’s social and economic impact increased in areas such as healthcare, finance, and education.⁴ The Internet may have connected many people in the world, but mobile gave them true global access. Mobile also allowed individuals and small and midsize enterprises to join a global economy that had long been dominated by big companies.

Mobile had a direct economic impact of $3 trillion on a global value chain in 2014, created 11 million jobs in the global value chain, and resulted in billions of R&D investments and startups.⁵ This transformation was the result of a huge investment from many players within IT and telecommunications that developed the core technologies, infrastructures, mobile networks, apps, and devices.

While behind mobile, the IoT is following a similar path. It started as a concept around 2000, is now in its second wave of development, and is experiencing exponential growth. Not only are IT and telecommunications companies investing in the IoT; so are players from outside the classical digital markets.

The IoT won’t remain behind mobile, however. It will spark a transformation that will exceed mobile’s by connecting everything together. We expect nothing less than a reinvention of interactions, communications, and services on a global scale, creating new jobs and opportunities.

As stated earlier, the IoT’s real value isn’t in the connections but in the data it creates. This massive digitization allows us to gather data and information and come up with decisions in the moment. When the IoT is integrated into the overall business strategy, a company has the opportunity to become a Live Business – when the entire business focuses on sensing and responding to customers in all the moments that matter most to them. Only companies that embrace the culture of a Live Business will take full advantage of the IoT.

How do you embrace that culture and create a live IoT strategy? Here are five steps to building that strategy.

Strategic Asset Management

Most companies today don’t have a real-time information repository of their assets, yet fixed assets can account for as much as one-third of all operating costs. Even in a highly automated manufacturing process, then, strategic IoT-based asset management can help save costs and resources and define a new path for innovation. With the IoT, businesses can monitor and evaluate asset usage patterns and maintenance routines, estimate current asset values, and find new ways to optimize asset use. Moreover, companies can share their assets with business partners, which will drive down costs and potentially uncover new business models. Companies can manage their assets with the IoT by optimizing routing for cars and trucks involved in logistics services, reducing machine downtime through remote maintenance and predictive analytics, and improving logistics and production planning by combining data from the IoT with demand and logistics.

Customer Experience

Digitization is radically changing the customer experience. By using the IoT as a direct feedback loop throughout the customer journey, businesses can enhance every purchase and interaction experience to transform passive consumers into live interactive partners and co-innovators. The true benefit of the IoT in this case is to offer a personalized and contextualized experience for customers in the moment.

Product and Service Experience

The IoT is enabling businesses to deliver more proactive and interactive products and services, which improves the brand experience and customer loyalty. Moreover, businesses can use the IoT to improve their products and services and co-innovate with customers, while remaining constantly in touch with them to understand their changing needs and wants.

Environmental Scanning

Sensor technology is constantly improving, which will allow more devices and machines to proactively monitor their environments and deliver a live view of the world around us at a much higher resolution. Companies will be able to use sensors to scan the environment to improve navigation, logistics, city planning, weather prediction, agricultural planning, and pollution management, for example. By combining real-time IoT information with existing data, organizations can react faster to change, create new insights, and develop new products and services.

Advanced Cooperation

Many devices and daily things around us will be enabled to interact with humans and with each other, creating a totally new experience in the physical world. We are already exploring connected and interactive cars and various forms of wearables.

As a Service

The move from products to services has been under way for some time, but digitization will speed up the transition, as the IoT allows companies to more directly serve their customers. The IoT will also help companies meet increasing customer demand for live service. Companies should use the potential offered by the IoT to develop new services along the product lifecycle and look for ways to create services using existing capabilities within the organization.

Resource-Optimized Innovation

Using the IoT, companies can make their products more interactive. They can monitor the actual use of their products, getting live feedback from customers and gathering data about longevity and performance. Products will no longer be designed once but will go through multiple iterations as part of a continuous, real-time innovation process. Not only will products become better over time, but customer loyalty will also be improved.

Omnichannel Customer Experience

Today’s digital customers demand a seamless shopping experience in all channels. Retailers and consumer-product companies must close any gaps between offline and online experiences. The digital IoT-driven channel will improve the brand experience and create opportunities for co-innovation.

Business Awareness

Today’s businesses are operating in a 24-hour real-time environment. Combined with a live-data analytics strategy, the IoT can be used as an antenna to reveal business changes. By getting faster and better information about demand changes and altered buying behavior, by predicting potential disruptions, and by identifying opportunities, companies will significantly increase their resilience in the marketplace.

Steps to Big Data Success

By Sudhi Sinha, Vice President of Product Development, Building Technology & Services, Johnson Controls

A great convergence occurred several years ago in information technology, with the cost and capacity of computer storage, processing, and networking all improving at the same time. This trifecta brought us to an inflection point where new technologies such as Hadoop could unlock unprecedented volumes of data and deliver new business value. That is the essence of the much-talked-about phenomenon known as “big data.”

In practical terms, big data means it is now feasible to collect, store, and use all available data, leading to new insights derived from ever-more sophisticated analytics. This presents a great opportunity to increase the value of existing data while also preparing the ground for the deluge of additional data expected to be unleashed by the Internet of Things, or IoT— the network of information generated by people, objects, and the environment.

How did we get to this place? Historically, large data sets needed to be structured and stored in a very specific way in order to then apply queries and analytics. From the columns and rows of what conceptually was much like a vast spreadsheet, deriving insights was an inflexible process where the very structure of the data was predefined and tended to make the range of conclusions all but inevitable.  In that environment, out-of-the-box thinking was almost impossible.

Today, big data technologies have overcome the limitations of past practices by breaking data sets into multiple components, distributing and analyzing those components on different processors, and then aggregating the results to deliver a meaningful picture of the whole that is not constrained by a specific, preordained order or structure.

As the ability to use all that data and run analytics on it has increased and improved, businesses are suddenly able to access new types of insights. It is the “art of the possible” for data.  Furthermore, now that the analysis encompasses all the data rather than just samples, the results are more accurate and   you can look at data in connection with more scenarios, sources, geographies, and applications. Your view of what is possible starts to broaden.

Combining Big Data and the IoT

Big data technologies are maturing, and they are widely applied across many industries. In automotives, for example, a manufacturer can incorporate data from warranty and maintenance activities or even a customer’s visit to the auto body shop to better understand patterns of failure.

Collecting data from dealers in different states is not new. But big data takes this activity to the next level. For example, marrying the actual weather data in a region to vehicle failure information allows manufacturers to begin developing predictive analytics. Somewhat simplistically, cars in Massachusetts may be found to suffer more cold-induced battery failures while vehicles in Texas may see more problems with air-conditioners.  But folding in other data streams flowing from the Internet of Things enables more nuanced predictions. Think how the somewhat obvious prediction that cars in cold climates suffer from battery failures and in hot climates experience air-conditioner problems can be enhanced by factoring in local traffic information, say, or sensor data from the car itself.

Consumer data is central to retail operations and to the airline, ticket, and travel industries. In these instances, you are trying to understand demographics and individual consumers in the context of location, season, trends, and so on. Big data is a powerful tool for bringing together what you know about those factors and integrating new sources of information – for example, sensors in a retail facility or weather for a travel site – to produce deeper insights.

Putting Big Data to Work in Your Business

To realize the value of big data and capitalize on the emerging  Internet of Things, follow this step-by-step approach:

1. Build a Strategy Framework.
  It is vital to develop a clear conceptual understanding of big data and how its potential might map to your own organization’s needs.  This should probably begin with a review of existing business intelligence efforts and data sources. But don’t stop there; look further and see whether there might be other data sources that aren’t being utilized or processes that could be better instrumented to provide valuable data. Remember, big data means BIG. More is generally better.  Think about the kind of insights big data might be able to provide, and then get ready for the next steps, when you will move ahead and engage the organization.

2. Create an “Opportunity Landscape.” 
 If the full potential of big data is a gold mine, the tactical projects that can get you started are gold coins.  The point is to focus initially on a small number of projects or initiatives with the potential for significant payback.  These could be aspects of your business that have not performed as expected and where data is available to potentially change the game.  When you’ve found the gold in those projects, you will be better able to gather resources to finance the gold mine and begin to extract widespread rewards for the organization.

3. Effectively Manage Big Data Projects. 
This means having a good grasp on the learning that may be transferable from other transformation and IT projects, as well as focusing on what is really unique.  Depending on the existing capabilities of your organization, big data may require investments in human capital—the IT experts and data scientists who can help ensure that you get the desired results. Because big data is by definition an evolving activity, having a robust project-management framework can help you keep things on track and moving in the right direction. That approach can also help you steer the initiative toward new and emerging opportunities.

4. Build the Right Technology Landscape. Even if you are an IT professional, the nuances associated with big data may be eye opening.  A big data initiative does not have to “break the bank” but it will likely require some specific investments.  The good news is that big data initiatives are generally less expensive and less complex than the massive data-warehouse projects that some organizations have built using traditional IT tools—a sort of “brute force” attempt to garner more value from corporate data. The best news is that starting small means your initial expenses can be quite low.

5. Build a Winning Team.
 Even in an era where technology is so critical, the human factor can make the difference between success and failure. Big data projects call upon a range of skills. Obviously, some of these are pure IT skills; others have to do with mastering the data science side of big data. But deep business knowledge is also vital – having the ability to dig into operational realities and discern issues that can be attacked with big data. Recruiting the people you will need for your big data projects, and then organizing, managing, and motivating them, is a case of making investments up front that will pay over the long term and yield future success.

6. Manage Your Investments and the Monetization of Your Data.  
The valuation and monetization of data is almost a “secret sauce” aspect of big data.  Having an approach to help you weigh the costs of big data with its benefits can help you make decisions that are most likely to be profitable and meaningful. This inquiry starts at a granular level and helps you develop an appreciation of the true value of data and its costs.  If you take this path you’ll never look at gigabytes the same way again, and you’ll begin to develop a practitioner’s instincts for harnessing information cost-effectively.

7. Effectively Drive Change.
 Your initial big data steps may not rock the boat too much, but over time, big data can upend assumptions, sometimes even assumptions that have been driving the entire business.  With big data, you have the potential to cut through the fog and know the things that have always seemed unknowable.  If that sounds hard to believe, think about how much data is not used effectively now. Add to that the new data that may become available in the near future, through sensors, better use of existing systems, and new external sources. To ensure that all of this data helps the organization, plan to implement change-management practices and start teaching your organization how to be more agile, adapting quickly to new assumptions driven by fresh insights.

8. Communicate Effectively.  
Change can only be mastered with effective communication. This isn’t simply a one-way street.  Listening is vital, too.  For an organization to ride the big data wave successfully, everyone should be “on board.” Only a small percentage of the organization may need new skills, but because big data can alter how business is conducted, everyone needs to be aware that change is afoot and to understand their stake in the future.

***

In the final analysis, big data is about opportunity. It is the embodiment of the old adage, “knowledge is power.” By rebuilding your business upon the collection and analysis of big data – data that will expand exponentially as the Internet of Things emerges and matures – you can create a better future.

Different ways that big data is transforming marketing and sales

With the Internet going mainstream 20 years ago, big data is transforming marketing and sales like never before.

Today, marketers and sales leaders are in the midst of a major technology-driven transformation. We have access to a flood of data, giving us visibility into customer behavior and effectiveness of our marketing programs. However, we are a creating a new silo of data with every marketing application that goes live. And so what we lack is a visibility into the end-to-end customer journey or the aggregated customer view. We need to know better, and this is why it is crucial to understand how big data is transforming marketing and sales.

Ways how big data is transforming marketing and sales

Identifying valuable opportunities

In order to discover opportunities, you need to pull in relevant data sets; not just from within the company, but also outside it. Once you have the data, next comes analytics. And analytics leaders believe in ‘destination thinking’, and not mass analysis of all the data collected. Destination thinking involves writing, in simple sentences, the questions you need answers for or the business problems you wish to solve. Big data and its analysis is transforming marketing and sales by going beyond the broad and vague goals, into a level of specificity. For example, a company may have 20 percent of the overall market, a micro market analysis may reveal that while it has 60 percent of share in some markets, its share in others may be as little as 10 percent.

Starting with the consumer decision journey

Consumers today surf multiple channels and use an array of devices, technologies, and tools to fulfill a task. Data collected from these sources is critical to understanding the decision journey of a customer; this helps in not just identifying new customers, but also retaining the existing ones. Let’s take the example of B2B companies understand the importance of mapping customer decision journey in marketing and sales outcomes. 35 percent of B2B pre-purchase activities are digital in nature, and so B2B companies need to invest in websites that can effectively communicate the value of their products, SEO technologies to find potential customers and social media for spotting new sales opportunities. The underlying idea is for marketing and sales leaders to use big data for forming complete pictures of their customers; thereby, creating products and messages that are relevant to them. Big data helps you gain clarity, deliver more personalized products and services, up the ROI on marketing spend, and lift sales.

Adding speed and simplicity

The rate at which data is growing worldwide is proving to be quite daunting for most marketing and sales leaders. However, approaches like predictive statistics, natural language mining, and machine learning that allow for processing of vast amounts of data, employing a self-learning process, help create better and more relevant interactions with customers. For this, companies need to invest in what is called as algorithmic marketing. Algorithmic marketing uses big data and lends speed and simplicity to your marketing and sales activities. For example, you cannot just track keywords automatically, but also update them every 15 seconds based on ad costs, customer behavior, or change in search terms used. Advanced analytics, when applied to big data, not just speeds up your efforts at marketing and sales, but also shields your customer service operator or field sales representative, from analytical complexity; all you have in hand are simple guidelines and recommended actions. Big data is a goldmine for marketing and sales leaders, waiting to be mined for umpteen possibilities. You need to think beyond immediate revenues to make the most of it.

Steps to Starting a Successful Business

1. Start a side hustle.

Most people underestimate how much time there is outside of work if you have a standard 9-5 job. If you are working at a job you dislike and want to be your own boss, stop complaining about your current terrible job and take action. Grab a cup of coffee after work and start hustling from 6 p.m to 2 a.m. on your new business venture.

If you are an artist, start posting YouTube videos about the details behind your painting process. Open up an ecommerce store on Shopify. Promote your products on Instagram. All of this can be done as a side hustle while you grow your business. Best of all, you are still collecting a paycheck from someone else.

2. Stop boozing.

If you are truly passionate about entrepreneurship, you will quickly understand that the weekends are the most productive time to get work done. You literally can put in 20 hours of work on the weekend to grow your brand. 

This isn't going to happen if you are going to the bars on Friday and Saturday night. First off, you are going to drain your bank account from buying shots for all of your buddies. Secondly, you are not going to be productive when you're hung over.

Start setting your alarm clock and for 6:30 a.m. on the weekends. Put in a full day of work. You won't have any regrets about missing a night or two out when you're in your 30s and have a million-dollar business.

3. Wake up early.

Thomas Jefferson once said, "The sun has not caught me in bed in 50 years." Apple CEO Tim Cook is known for getting up early and sending out company emails at 4:30 in the morning. The youngest CEO in the NBA, Brett Yormark, gets up at 3:30 in the morning in order to get to the office by 4:30.

When it comes to business, the early bird catches the worm. You can actually be playing offense rather than defense, which will allow you to work on growing your business.

Make sure to put your alarm clock on the opposite side of the room, which will force you to get out of bed and not hit the snooze button. Do 50 pushups within 5 minutes of turning of your alarm clock to truly wake yourself up. If you need extra motivation for waking up early, follow Before 5 AM on Instagram.

4. Build your personal brand. 

It blows my mind how much time people spend on social media promoting themselves yet they don't have a website that gives people more information or a place where people can get in contact with them. 

Everyone should see if their first and last name is available for purchase on GoDaddy. If you have a common name, insert your middle name and your craft as part of the URL (example: SarahSmithNYCArtist.com). The next time you go into a business meeting, you'll be amazed how much more impressed people will be by your professional website.

5. Become an expert by contributing content.

In addition to owning a marketing and app development agency, I am also the partner of an ecommerce skincare website that sells dermatology strength products. We are constantly trying to get media mentions from Glamour, Bustle and Teen Vogue. Do you know what I discovered? A ton of the writers on these nationally recognized sites are college students!

If you love sports, start reaching out to sites like YardBarker to become a contributor. If you love fashion, what's holding you back for writing for Teen Vogue? This will boost your personal brand, establish credibility and present new opportunities you never imagined. 

6. Talk it out. 

When you are an entrepreneur, you live on a lonely island. Nobody will ever realize just how much hustle you put in on a daily basis. Your friends who work a 9-5 job won't understand how your business means the world to you. 

Since entrepreneurship can be lonely, make sure you have someone you can air it out with. Whether it is your girlfriend or boyfriend, mom or dad, or just a mentor you can trust, in order to grow, you need to be able to have a sounding board along the journey.

7. Have patience.

The issue with the digital age that we live in is that people are impatient. They expect results yesterday.

Bill Gates was famously quoted saying, "Most people overestimate what they can do in one year and underestimate what they can do in ten years." 

In order to build a business, you need to be patient for the long run. Don't think about where you want your business to be in a year; think about your goals for 10 years down the road and how you are going to execute to make all of your ambitions come to fruition. If you are in your 20s, what will differentiate you as an entrepreneur is patience. Make sure to move fast on getting stuff done but be patient for the long-term gains.

I've owned my company for seven years now. I know that 96 percent of businesses fail before turning 10. I keep that statistic in my mind each and every day because I want to be part of the 4 percent that succeed past 10.

Each year you celebrate your business anniversary, your muscles will continue to grow.

8. Make money. 

I can't stand all of the people who claim they are entrepreneurs. One simple question separates the contenders from the pretenders: "Are you generating revenue?" There are so many people out there who claim to be crushing it with their "entrepreneurial journey" but when you dig into it, they aren't generating a penny in revenue. I'm not even getting into the details of generating a profit, which is what every business must do to survive. 

If you aren't making money with your business, you need to start and you need to start soon. Just like a hockey team needs to score goals in order to win a game, your business needs to make money to actually be considered a business. Stop with the pretend stuff and start selling your product or service. 

9. Start meeting with millionaires.

On a monthly basis, start meeting with a millionaire for coffee. You'll be amazed that there is no secret sauce to succeed in business. You'll learn that hard work leads to better luck and success. You'll also become inspired and motivated while listening to someone who has succeeded in the business world.

If you try and meet with Evan Spiegal from Snapchat, you aren't going to have much luck. You'd be amazed, though, how many CEOs of billion- and multi-million-dollar companies would be willing to meet with you if you are persistent. 

Don't think for a second that you are the only one gaining value from the meeting. The CEOs and founders that you meet with -- who will likely be in their 40s, 50s and 60s -- will be asking you a lot of questions. The reason? Their customers are most likely your age, and they understand the spending power of millennials and Gen Zers.

10. Become an SEO and paid advertising expert. 

Do you want to know a more efficient way to grow your business than attending outdated networking events? Rank towards the top of Google so people will contact you directly for business inquires. Whether you just graduated from law school and plan on opening up your own practice or you are selling customized dog collars, SEO and paid ads can literally help any business.

This is one of the main reasons I've been able to grow my marketing agency quickly and efficiently. In the Columbus, Ohio markets, we crush it for terms like "social media company" and "SEO Company."

There is no college degree or class you have to take to become an SEO or PPC expert. You just have to get your hands dirty, read articles and watch videos. If you are starting a business, you need to be well versed in this area so you can generate leads and sales online each and every day.

11. Be cheap.

Earlier in the article, I talked about how you can save money by not going to the bars every weekend. Do everything you can to be frugal with your money. You never know when the economy will turn south or when your biggest client or customer will drop off. If you are spending lavishly, you won't be well prepared for tough times. 

Start eating more peanut butter and jelly sandwiches and eat out less. Stop buying clothes at Nordstrom and start shopping at TJ Maxx.

Every dollar is so important when you are starting a business. There's a reason Warren Buffett has lived in the same house in Omaha, Neb., that he bought for $58,000 in 1958. The real winners in business are smart with their money. 

Top Most and Hot Big Data Technologies

Forrester’s TechRadar methodology evaluates the potential success of each technology and all 10 above are projected to have “significant success.” In addition, each technology is placed in a specific maturity phase—from creation to decline—based on the level of development of its technology ecosystem. The first 8 technologies above are considered to be in the Growth stage and the last 2 in the Survival stage.

Here is the 10 hottest big data technologies based on Forrester’s analysis:

1. Predictive analytics: software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources to improve business performance or mitigate risk.
2. NoSQL databases: key-value, document, and graph databases.
3. Search and knowledge discovery: tools and technologies to support self-service extraction of information and new insights from large repositories of unstructured and structured data that resides in multiple sources such as file systems, databases, streams, APIs, and other platforms and applications.
4. Stream analytics: software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple disparate live data sources and in any data format.
5. In-memory data fabric: provides low-latency access and processing of large quantities of data by distributing data across the dynamic random access memory (DRAM), Flash, or SSD of a distributed computer system.
6. Distributed file stores: a computer network where data is stored on more than one node, often in a replicated fashion, for redundancy and performance.
7. Data virtualization: a technology that delivers information from various data sources, including big data sources such as Hadoop and distributed data stores in real-time and near-real time.
8. Data integration: tools for data orchestration across solutions such as Amazon Elastic MapReduce (EMR), Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB.
9. Data preparation: software that eases the burden of sourcing, shaping, cleansing, and sharing diverse and messy data sets to accelerate data’s usefulness for analytics.
10. Data quality: products that conduct data cleansing and enrichment on large, high-velocity data sets, using parallel operations on distributed data stores and databases.

Best Big Data Companies

Tableau

Originally spun out of Stanford University as a research project, Tableau started out by offering visualization techniques for exploring and analyzing relational databases and data cubes and has expanded to include Big Data research. It offers visualization of data from any source, from Hadoop to Excel files, unlike some visualization products that only work with certain sources, and works on everything from a PC to an iPhone.

New Relic

New Relic uses a SaaS model for monitoring Web and mobile applications in real-time that run in the cloud, on-premises, or in a hybrid mix. It uses more than 50 plug-ins from technology partners to connect to its monitoring dashboard. The plug-ins include PaaS/cloud services, caching, database, Web servers and queuing. Its Insights software for analysis works across the entire New Relic product line, and the company offers a product called Insights Data Explorer that is designed to make it easier for everyone on a software team to explore Insights events.

Alation

Alation crawls an enterprise to catalog every bit of information it finds and then centralizes the organization's knowledge of data, automatically capturing information on what the data describes, where the data comes from, who's using it and how it's used. In other words, it turns all your data into metadata, and allows for fast searches using English words and not computer strings. The company's products provide collaborative analytics for faster insight, a unified means of search, provides a more optimized data structure of the company's data, and assists in better data governance.

Teradata

Teradata has built a portfolio of Big Data apps into what it calls its Unified Data Architecture, which includes Teradata QueryGrid, Teradata Listener, Teradata Unity and Teradata Viewpoint. QueryGrid provides a seamless data fabric across new and existing analytic engines, including Hadoop. Listener is the primary ingestion framework for organizations with multiple data streams, Unity is a portfolio of four integrated products for managing data flow throughout the process, and Viewpoint is a custom Web-based dashboard of tools to manage the Teradata environment.

VMware

VMware has incorporated Big Data into its flagship virtualization product, called VMware vSphere Big Data Extensions. BDE is a virtual appliance that enables administrators to deploy and manage the Hadoop clusters under vSphere. It supports a number of Hadoop distributions, including Apache, Cloudera, Hortonworks, MapR and Pivotal.

Splunk

Splunk Enterprise started out as a log analysis tool but has since expanded its focus and now focuses on machine data analytics to make the information useable by anyone. It can monitor online end-to-end transactions, study customer behavior and usage of services in real time, monitor for security threats, and identify spot trends and sentiment analysis on social platforms.

IBM

Besides its mainframe and Power systems, IBM offers cloud services for massive compute scale through its Softlayer subsidiary. On the software side, its DB2, Informix and InfoSphere database software all support Big Data analytics and Cognos and SPSS analytics software specialize in BI and data insight. IBM also offers InfoSphere, the basic platform for building data integration and data warehousing used in a BD scenario.

Striim

Formerly known as WebAction, Striim is a real-time, data streaming analytics software platform that reads in data from multiple sources such as databases, log files, applications and IoT sensors and allows customers to react instantly. Enterprises can filter, transform, aggregate and enrich data as it is coming in, organizing it in-memory before it ever lands on disk.

SAP

SAP's main Big Data tool is its HANA in-memory relational database, which the company says can run analytics on 80 terabytes of data and integrates with Hadoop. Although HANA is a row-and-column database, it can perform advanced analytics, like predictive analytics, spatial data processing, text analytics, text search, streaming analytics, and graph data processing and has ETL (Extract, Transform, and Load) capabilities.

While some companies specialize in one or few sources of data, SAP deals with data from a wide range of sources, including data from sensors, machine logs and other equipment; human generated data – social, point of sale (POS), ERP, emails documents and other things that make up enterprise data.

Alpine Data Labs

A creation of Greenplum employees, Alpine Data Labs puts an easy-to-use advanced analytics interface on Apache Hadoop to provide a collaborative, visual environment for building analytics workflow and predictive models that anyone can use, rather than requiring a high-priced data scientist to program the analytics.

Oracle

Oracle has its Big Data Appliance that combines an Intel server with a number of Oracle software products. They include Oracle NoSQL Database, Apache Hadoop, Oracle Data Integrator with Application Adapter for Hadoop, Oracle Loader for Hadoop, Oracle R Enterprise tool, which uses the R programming language and software environment for statistical computing and publication-quality graphics, Oracle Linux and Oracle Java Hotspot Virtual Machine.

Alteryx

Calling itself the leader in self-service data analytics, Alteryx's software is meant for the business user and not the data scientist. It allows them to blend data from multiple and potentially disparate sources, analyze it and share it so that actions can be taken. Queries can be made from anything from a history of sales transactions to social media activity.

Splice Machine

Splice Machine bills itself as the provider of the only Hadoop relationship database management system (RDBMS). It can act as a general-purpose database that can replace Oracle, MySQL or SQL Server databases for various workloads on Hadoop. The latest version, 2.0, added Spark, which does all analytics in memory instead of on disk. Version 2.0 also added the ability to route work to one of two processing engines either OLTP or OLAP.

Pentaho

Pentaho is a suite of open source-based tools for business analytics that has expanded to cover Big Data. The suite offers data integration, OLAP services, reporting, a dashboard, data mining and ETL capabilities.

Pentaho for Big Data is a data integration tool based specifically designed for executing ETL jobs in and out of Big Data environments such as Apache Hadoop or Hadoop distributions on Amazon, Cloudera, EMC Greenplum, MapR, and Hortonworks. It also supports NoSQL data sources such as MongoDB and HBase. The company was acquired by Hitachi Data Systems in 2015 but continues to operate as a separate subsidiary.

SiSense

SiSense sells its Prism to the largest enterprises and some SMBs alike because of its small ElastiCube product, a high-performance analytical database tuned specifically for real-time analytics. ElastiCubes are super-fast data stores that are specifically designed for extensive querying. They are positioned as a cheaper alternative to HP's Vertica systems.

Thoughtworks

Thoughtworks incorporates Agile software development principals into building Big Data applications through its Agile Analytics product. Agile Analytics helps companies build applications for data warehousing and business intelligence using the fast paced Agile process for quick and continuous delivery of newer applications to extract insight from data.

Tibco Jaspersoft

Tibco's Jaspersoft subsidiary has introduced an hourly offering on Amazon's Cloud where you can buy analytics starting at $0.48 per hour. The company is also big on embedded its analytics – having done so with 130,000 production applications worldwide, used by organizations such as Red Hat, CA, Verizon, Tata, Groupon, British Telecom, Virgin, and the U.S. Navy.

Amazon Web Services

Amazon has a number of enterprise Big Data platforms, including the Hadoop-based Elastic MapReduce, Kinesis Firehose for streaming massive amounts of data into AWS, Kinesis Analytics to analyze the data, DynamoDB big data database, NoSQL and HBase, and the Redshift massively parallel data warehouse. All of these services work within its greater Amazon Web Services offerings.

Most significant, AWS is attempting to woo legacy database customers to its newer offering. Experts disagree on how successful AWS will be in this effort, but it is clearly a highly aggressive competitive move.

Microsoft

Microsoft's Big Data strategy is fairly broad and has grown fast. It has a partnership with Hortonworks and offers the HDInsights tool based for analyzing structured and unstructured data on Hortonworks Data Platform. Microsoft also offers the iTrend platform for dynamic reporting of campaigns, brands and individual products. SQL Server 2016 comes with a connector to Hadoop for Big Data processing, and Microsoft recently acquired Revolution Analytics, which made the only Big Data analytics platform written in R, a programming language for building Big Data apps without requiring the skills of a data scientist.

Google

Google continues to expand on its Big Data analytics offerings, starting with BigQuery, a cloud-based analytics platform for quickly analyzing very large datasets. BigQuery is serverless, so there is no infrastructure to manage and you don't need a database administrator, it uses a pay-as-you-go model.

Google also offers Dataflow, a real time data processing service, Dataproc, a Hadoop/Spark-based service, Pub/Sub to connect your services to Google messaging, and Genomics, which is focused on genomic sciences.

Mu Sigma

Mu Sigma offers an analytics services framework that looks at tables and tables and answers questions for the firm on issues like improved sales and marketing. It cleans up client data to show only relevant data, uses the data to understand it, generates insights from it and gives recommendations to the client. Mu Sigma tries to understand how the business actually works and then identifies where the problem actually is.

HP Enterprise

HP Enterprise has built up a considerable portfolio of Big Data products in a very short time. Its main product is the Vertica Analytics Platform, designed to manage large, fast-growing volumes of structured data and provide very fast query performance on Hadoop and SQL Analytics for petabyte scalability.

HPE IDOL software provides a single environment for structured, semi-structured and unstructured data. It supports hybrid analytics leveraging statistical techniques and Natural Language Processing (NLP).

HPE has a number of hardware products, including HPE Moonshot, the ultra-converged workload servers, the HPE Apollo 4000 purpose-built server for Big Data, analytics and object storage. HPE ConvergedSystem is designed for SAP HANA workloads and HPE 3PAR StoreServ 20000 stores analyzed data, addressing existing workload demands and future growth.

Big Panda

BigPanda offers a data science algorithm-based platform specifically for IT and DevOps staff that is specifically geared toward addressing alert overload. One of the many sources of Big Data is logs, and they can quickly get out of hand with redundant or false alerts. The company noticed that developers were being overwhelmed with alerts from their logs and had no idea which were real and which were false flags. BigPanda filters down that overload to just the meaningful alerts, allowing IT to react quicker to real problems.

Cogito

A highly vertical but important service, Cogito Dialog uses behavioral analytics technology, including analysis of everything from customer emails to social media to analysis of the human voice, to help phone support personnel improve their communications while on the phone with customers and to help organizations better manage agent performance.

Datameer

Datameer claims its end-to-end data analytics solution for Hadoop enables business users to discover insights in any data via wizard-based data integration, iterative point-and-click analytics, and drag-and-drop visualizations, regardless of the data type, size, or source.