Businesses rely on big data to understand the various patterns in the behavior of the customers. One of the most important sides to handling big data is big data visualization. When there are images added to all the data, understanding the pattern becomes a less herculean task. It helps process various varieties of data types. External filters may be applied to receive the expected results. It also allows the user to collaborate with other software to ensure efficiency and maintain timelines.
There are various tools that you visualize all of this data through visualization. Here are some that would fit any requirement.
It handles over ten programming languages and has an easy interface. It instantly delivers the images that would make the task of analyzing the data easier in real-time. If you wish to handle multiple frameworks at once, this would fit the bill. You may even upload it online for the public to collaborate.
This one is the leader in the Big Data visualization industry. It is known for providing efficient data in an interactive format. There are no algorithms that this software can escape. They can be integrated with various other platforms for ease of use. Using this, you may make the most sound decisions for your organization.
There are various types of visualization that you may choose from. If you feel stuck anywhere, they even offer their help. If you require the services of special customization, you may reach out to them. It is compatible with most software types.
All these solutions are just a scratch on the surface. There are various other options available to meet your unique requirements. To make the best business decision, understanding data is imperative. Using these tools, you would be able to drive traffic to your case and monetize the opportunity. It also helps identify various gaps and helps fix them to receive quick results.
There is so much opportunity for growth that big data offers. By using the tools and the techniques that are available, you can improve your efficiency. It is a known fact that when the data is represented visually, it is more understandable to the stakeholders. So, make the most of the technology available in front of you.
The big data technology and services market is expected to reach $48.6 billion in annual spending by 2019. The increasing spend is coming with business intelligence (BI) and analytics requirements that are breaking traditional data warehousing platforms. In a recent study, 46 percent of the senior executives surveyed acknowledged that their traditional technologies were not architected to handle modern workloads.
It’s a problem that is being compounded by an unbound growth of data, and new sources of data only make the quest for accurate insights more difficult. These traditional architectures forced more than 25 percent of respondents to discard data to get analytic insights because they couldn’t scale to process the volume of data being collected. In addition, 31 percent of respondents said traditional architectures are not designed for new workloads, and 29 percent said it is too expensive to scale. Customers are suffering due to the innovator’s dilemma these traditional vendors find themselves in. Is this the tipping point?
More data has been created in the last few years alone than in the entire history of mankind, and it’s not slowing down. It has become imperative for organizations to access, process, and analyze troves of data in real-time to make effective business decisions in a contextual and timely manner, but it is no longer viable for business leaders to fall back on traditional data management platforms. Many have started to turn their attention from static legacy frameworks to more modern solutions like Hadoop and Spark, but have found limited success. While these may be cost-effective tools for storing and sifting through massive amounts of data, 30 percent of respondents in the aforementioned study said these new Hadoop analytics architectures are not yet ready to provide the enterprise-grade performance needed to effectively execute advanced real-time analytics.
Amid this platform stagnation, BI tools are also suffering, and this one-two punch is leaving businesses overwhelmed on dual fronts. Just 40 percent of survey respondents said their BI tools were working well with historical data sets, and 32 percent acknowledged these tools were overmatched by increasing data volumes.
This suffering extends beyond functionality, ultimately forcing enterprises to pay increasing sums for solutions that are getting less effective over time: 47 percent of respondents said the cost to maintain traditional systems continues to rise. However, companies are apprehensive to rip and replace their faltering legacy solutions: only 32 percent of survey respondents indicated that they would supplant them with a modern tool. This is often the case because organizations have already invested so much in their existing data management platforms and they are reluctant to write them off as a loss. In addition, 62 percent of respondents have decided to augment these traditional systems with modern architectures to meet the needs of their modern analytic workloads. This often results in relegating traditional systems to transaction processing workloads – much like what happened in the shift from mainframes to x86-based servers.
Opportunity in the Downfall of Traditional Management Platforms
There is still opportunity as businesses navigate the challenging waters of the modern data era. The opening to capitalize on business opportunities through customer analytics and Internet-of-Things strategies is there, but organizations first must overcome the barriers created by the breakdown of legacy solutions and find ways to better manage the growing stress of big data. Until that is achieved, they will continue to sit on a wealth of untapped data, limited by the commercial and technical constraints of their traditional management systems. So what will it take to break free?
There is no magic bullet, but the best answer lies in the amalgamation of existing and new technologies. Businesses want to use their domain expertise and continue leveraging their legacy investments while still enjoying the benefits of more modern environments like Hadoop – and there is a way to do this through solutions like SQL-in-Hadoop. Hadoop already can address the issues of cost and size for data storage. All that’s left is turning it into a big data analytics platform adept for the enterprise.
Fortunately, existing applications and queries based on SQL – the lingua franca of relational databases – don’t have to be rewritten to work with Hadoop, and data don’t have to be brought out of it either, making Hadoop the perfect complement for legacy deployments. Using SQL also enables users to leverage existing BI and visualization tools, not to mention existing dashboards and reports. In all, it allows organizations who have invested extensively in legacy data management platforms to retain these existing systems and maximize their ROI while working to match the demands of analyzing today’s big data with more modern solutions.
Just like mainframes that could not keep up with compute and scale requirements, traditional data management systems are floundering in the depths created by the current waves of data workloads. They are pinned by architectural limitations and expensive commercial models. But due to extenuating financial circumstances – and the lack of an enterprise-ready modern alternative – they will not be replaced soon. Perhaps it’s time for organizations to invest those dollars in a different approach that leverages new technology to complement the faltering data-management platforms that have proven so hard to replace.
Ashish Gupta joined Actian in 2013, where he is responsible for marketing and business development. Ashish brings more than 21 years of experience in enterprise software companies, where he focused on creating go-to-market approaches that scale rapidly and building product portfolios that became category leaders in the industry. Ashish was formerly at Vidyo, where the company grew to be the leading software-based videoconferencing platform and was named to the Wall Street Journal’s “Next Big Thing” list for three years and was selected as a “Tech Innovator” by the World Economic Forum.
Previously, Ashish led the Business Development and Strategy, Marketing, and Corporate Sales teams for Microsoft Office Division’s Unified Communications Group, responsible for introducing the industry-leading Microsoft Lync product. Prior to Microsoft, Ashish was VP of Product and Solutions at Alcatel/Genesys Telecommunications and VP of Marketing and Business Development at Telera Inc. (acquired by Alcatel), and management consultant for Braxton/Deloitte Consulting. He also held marketing leadership positions at HP and Covad. He holds an MBA from UCLA and a bachelor’s degree in Economics and Computer Science from Grinnell College.
The data that we produce through just browsing online is immense and inevitable. If you want to keep abreast with the latest technology that is taking the world by storm, stay back and go through this list of the latest developments in the world of big data.
Using data for service
The data that is obtained from various sources online is stored in specified methods. Lately, there is a new service in the name of Data as a service that gives a lot of information upon demand. It is trending in the market and has made it simple to undertake various business activities or sharing of data.
Working the AI
The concept of responsible artificial intelligence can pick up the most complex algorithms in no time at all. It would bring efficiency to a job in shorter timelines. Upcoming businesses, that rely on data, can benefit the most from this undertaking.
All those analytics that big data offers has been the propelling point for many companies to set themselves apart from the competition. It helps them understand the various issues that might crop up in the process and understand why their target seems further than expected. When predictive analytics are applied, it helps the corporations gain a perspective of what the future holds. Many times, it has helped the big companies predict the next moves of their potential customers.
If we employ the traditional techniques to process all that data, it can take a huge toll on the timeline of the company. Enter quantum computing and you can quicken and hasten all the processes and welcome timely decisions.
If you are looking to reduce the amount of burden and connection that happens between the server and computer, you should look up edge computing. It reduces the load on the bandwidth that increases with large amounts of data. It allows the organization to cut down on the development costs while enabling the software to run in the most remote points.
The fabric of data
All the collection of the data and the architecture that it is based on is called fabric data. It helps the organization transform digitally and eases data storage. With this, the sharing of data within the premises becomes simpler.
Every second, the technology of big data is changing. To get a head start on the competitors, one has to utilize all this technology that we have in our access. To succeed in the business, and utilize the time to convert customers is all we can focus on if we have big data under our wing. Hence, ensure to take a look at this list and find out how it helps you.
These days, markets have been inundated with different tools relating to big data. They offer better efficiency and help reduce the time burdens on the organizations. Here are the tools that have been handpicked that would help you.
This software offers the best of everything in one. You may gain access to multiple platforms. It would help you gain information on every data source. You may handle multiple tasks at the same time, with this on your side.
If you are looking to hasten your data process, this is it. It would offer all the flexibility in handling data, that you require to make your job easier. You can extract different data from various different sources. It allows one to scale up the servers and perform higher jobs.
If you are looking for a data process that will allow you to scale your project and enhance your performance, go for this one. There are optimized libraries for C++. It handles even the most complex tasks.
This one is a free open system for any organization. Its capabilities when it comes to computation are insane. It is undoubtedly the best big data tool out there. It is very easy to run big data analysis on this one.
This is a single platform for various purposes. It has been optimized for it to be used for the cloud and offers complete security. It even complies with the various policies and offers protection to the data.
If you are looking for an open-source that can manage third parties, look no further than Cassandra. To enable the various aspects of the tolerance, the data can be repeated. It takes care of low latency too.
If you are looking for something quicker than your regular, Stats iQ would help. It can comply and assess large amounts of data at one go. It also creates various graphs and finds relations between the various aspects of the data. It makes the whole analysis easier.
This one is made on a single node and gives the benefits of any database. The interface that they offer is simple to understand for everyone, with straightforward options. Also, it can connect the user with multiple servers at the same time.
There are many open sources available for your big data applications. But you should choose the one that fits your business purpose. It is a great practice to try them all before you finalize and rely on one. Big data analytics have made our lives easier multifold. So don’t be surprised if you have unsurpassed development in this field quicker than you anticipated.
For the last few years, we have been hearing about the benefits (and all the permutations!) of the cloud. And at this point, many of us agree the benefits are sizable and beneficial. So, now that you have finally agreed that it’s time to move to the cloud, what’s next?
First off, it’s worth noting that moving to the cloud doesn’t have to be all-or-nothing. In fact, the best strategy often is to start by solving a specific problem or taking advantage of a good opportunity. Companies that weren’t “born in the cloud” – meaning any company more than a couple of years old – need a plan for going cloud. What new approaches will you adopt in the cloud? What systems will you sunset and transition to cloud in the next incarnation? What systems work today and will always stay as they are? You may never be 100 percent cloud – and that’s OK.
But to begin to go cloud, you do need a plan. And to help, here are a few strategies and things to think about as you begin your transition to the cloud.
Be Clear on the Problem you are Solving
Moving to the cloud should be driven by a real business problem, not by an abstract desire to be in the cloud. For example, do you need a new HR management solution? Consider starting there. Evaluate the cloud offerings in that space, like Workday, SuccessFactors, and others. You’ll likely have a faster implementation by going cloud, which means you’ll get value fast and you’ll be able to start your transition without ripping out something that’s working. And chances are that you’ll save money in the process.
Leverage the Cloud to Rethink the Way you do Things
One of the major advantages of cloud services is that they offer a completely new way of doing things, whether in capabilities or pricing. If you have decided to move a system to the cloud, don’t just replicate what you already have. Think of ways to leverage flexible pricing, elasticity, and instant provisioning.
Cloud data warehouses are one example of new approaches to old problems. Systems like Amazon Redshift and Google BigQuery can be set up in minutes, as opposed to weeks, and can scale to fit the size of your data. These systems are optimized for analytics and they offer a path to insight from big data from devices, social media, or machine systems.
Be as Flexible as the Cloud
The cloud is in a stage of rapid evolution. You have the possibility of prototyping as you go, and adding volume when you have it right. Keep an eye on new technologies and see how you can fit them into your workflows. Your best architecture today may not be your best architecture in a year, or even six months. A bit of tweaking can save you a lot of money.
As you consider new services, take advantage of the flexibility in the cloud. Elasticity is a characteristic of many cloud services. This means you can use (and pay for) a small amount at first and then scale dramatically when your concept is proven out. In the cloud, you can try things out without having to commit massive infrastructure or licensing costs up front.
You also can take advantage of different services to change your cost structure. For example, shifting your file system storage from Amazon S3 to the Amazon Glacier product, which offers slower access at a better cost, can save two thirds of your storage costs for your data lake.
Plan for Growth
One of the advantages of a cloud infrastructure is that you can scale up easily – as long as you have the right infrastructure. Take the time up front to get your systems working, as you want them, whether they are cloud applications, data, or analytics.
For example, you may be able to scale up your data collection overnight in the cloud – but if you’ve set up the wrong schema for collecting it, you’ll have a lot of fixing to do. If your business starts growing, a good system can go a long way with you – but a bad one will only add to your headaches.
Give your Users a Hand (or at Least a Single Sign-on Solution)
One of the challenges with moving to the cloud is that your users may end up with a number of different username and password combinations to remember. Luckily, there’s an app for that. Single Sign-On (SSO) solutions like OneLogin and others let your users use one password for many applications. This can significantly reduce headaches and make users more open to adopting new solutions. It’s a good idea to favor solutions that use SAML or OAuth, so that you can make use of an SSO solution when you are ready.
Enable your Organization Broadly
Consider ways to allow many people to leverage the cloud. The cloud makes mobile access easy, so think about which applications would be more valuable if you integrated them into the flow of business better. For example, could you give your sales team mobile access to the CRM system, or recruiters access to the HR system for when they are at events?
You can add even more value by broadening access to data. If you are building a data infrastructure in the cloud, think about how your businesspeople will be able to use that data. If you are moving to cloud applications, think about how you’ll integrate the data with other data in your enterprise. Otherwise, the limiting factor in your cloud infrastructure will be the time of your data scientists.
One way to do this is to offer access to data, and the insights from it, via an analytics platform that can connect to several different kinds of data. This helps people who know your business and who have the appropriate security access get value from your data, and from the cloud. And you don’t have to change your analytics platform every time you adopt a new data service.
Those are just a few strategies for moving to the cloud. The good news about the cloud is that it’s flexible, so different approaches will work. Be intentional, be incremental, and enjoy the atmosphere.
Ellie Fields is the Vice President of Product Marketing at Tableau. Ellie joined Tableau in the early stages of the company in 2008 and is part of the core team that has fueled the growth and success of Tableau’s products. In particular, she launched and oversees the development and growth of Tableau Public, which has served more than 200 million impressions. Tableau Public is a unique product, popular among journalists, developed for anyone that wants to easily publish interactive data on the web. Ellie also helped launch the company’s new cloud analytics product, Tableau Online. Prior to Tableau, Ellie worked in product management at Microsoft and as an associate in late-stage venture capital. Ellie hold B.S. and B.A. degrees in Engineering from Rice University and an MBA from the Stanford Graduate School of Business.
– See more at: http://data-informed.com/you-bought-into-the-cloud-now-what/#sthash.u3QcuAVP.dpuf
As several industries now step into the digital world, they seek assistance from various software experts to ease their work and functionalities. Data scientists play an essential role in managing the overflowing data they receive every moment to record, analyze and interpret various results. Do you think this job is similar for every organization and scientist? Here are a few tips that can help you imbibe yourself in your organization to emerge as an efficient data scientist.
Understand the organization’s work and nature
Every business and industry are different from the others, making the incoming data also different for all. If you wish to obtain the perfect data, you need to understand the core work nature of your industry. You can begin with niches you have a great insight into or those related to your academic studies.
For example, an e-commerce platform concentrates on the customer likes and feedbacks for its products. But a pharmaceutical manufacturer has to focus on the application and success rate of the medicines launched.
Acquire expertise in programming languages and database tools
The major role of a data scientist is to obtain data and compute results from it. Thankfully, you don’t need a pen and paper as modern digital tools are readily available for you. Programmed and developed with advanced AI, machine learning, and natural learning algorithms, these computational tools, and databases effectively work on simple commands.
Though they are ready-to-use apps, you must have ample knowledge in programming languages like python, Java, R-programming, or Scada to understand and modify the functions if required. You should also know about online database software like Hadoop, cloud workspaces, or Oracle DBMS to handle and store data effectively.
Keep track of current work functions to obtain apt data
Business trends and strategies change with time and demand. Though your job is to keep track and notify about the changes, you should simultaneously keep track of the new business strategies and principles applied. As the services of the business change for the public, you should also change the data you are obtaining to ensure an effective interpretation every time.
Upgrade, and upgrade yourself constantly
Data scientists work on modern platforms and constantly interact with the public. As new tools and databases are springing up every day, you need to be updated to implement the latest ones. Even though you are using some existing software, you should explore more of its features, ensure to update it to the latest version, and check out for cyber threats.
As a developing scientist, you should timely upgrade your technical skills with the advanced programming languages and software developments, as the later versions of the tools or the new ones launching work on them.
Work and socialize with the business staff and owners
Your job in a business sector might seem distinct from the core employees working for the product or service development. Though your tasks are related to computation, statistics, and analysis, you should be interactive and communicative with other employees and departments of your business. It can help you work efficiently, obtain easy channels to get enough data, and produce accurate results as demanded.
As the NoSQL sector continues to attract attention, graph databases are generating real and lasting excitement. In fact, interest in this sector has grown by a whopping 500 percent in the last two years alone. Forrester Research has reported that graph databases – the fastest-growing category in database management systems – will reach more than 25 percent of enterprises by 2017.
Despite their market momentum, some people still consider graph databases to be mysterious. But graph databases use intuitive principles that are similar to tasks we perform on a daily basis. Relational database management systems, on the other hand, have a comparatively steep learning curve. If you have ever worked out a route on a mass transit map or followed a family tree, you have manually run your own graph-based query.
In fact, it’s likely that you have come across a product or service powered by a graph database within the last few hours. Many everyday businesses have created new products and services and re-imagined existing ones by bringing data relationships to the fore. That’s because graph databases model, store, and query both data and their relationships, which is crucial for next-generation applications that feature use cases such as real-time recommendations, graph-based search, and identity and access management.
For example, Walmart, which deals with almost 250 million customers weekly through its 11,000 stores across 27 countries and through its retail websites in 10 countries, wanted to understand the behavior and preferences of online buyers with enough speed and depth to make real-time, personalized, “you may also like” recommendations. By using a graph database, Walmart is able to connect masses of complex buyer and product data quickly to gain insight into customer needs and product trends.
Zephyr Health, a San Francisco-based software company offering a data analytics platform for pharmaceutical, biotech, and medical device companies, sought to enable customers to unlock more value from their data relationships. Doing so would enable pharmaceutical companies, for example, to find the right doctors for a clinical trial by understanding relationships among a complex mix of public and private data such as specialty, geography, and clinical trial history.
Old-school SQL databases were not up to the task. Traditional SQL databases don’t handle data relationships well, and most NoSQL databases don’t handle data relationships at all. Nor are they well equipped to handle data that’s always changing – such as streams of new information coming in from doctor’s surveys.
Zephyr turned to a graph database for its capability and scale. Graph databases are designed to easily model and navigate networks of data with extremely high performance.
To fully appreciate the value of the graph, consider that early adopters of graph databases such as Facebook and LinkedIn became household names and unrivaled leaders in their sectors.
A “graph” can be thought of like a whiteboard sketch: When you draw on a whiteboard with circles and lines, sketching out data, you are drawing a graph. Graph databases store and process data within the structure you have drawn, providing performance advantages and making it easy to evolve the data model.
The Seven Bridges Puzzle
Far from being a recent data handling development, graph theory is nearly 300 years old and can be traced to Swiss mathematician Leonhard Euler. Euler was looking to solve an old riddle known as the “Seven Bridges of Königsberg.” Set on the Pregel River, the city of Königsberg included two large islands connected to each other and the mainland by seven bridges. The challenge was to map a route through the city that would cross each bridge only once. Euler realized that by reducing the problem to its basics, eliminating all features except landmasses and the bridges connecting them, he could develop a mathematical structure that proved such a walk was impossible.
Today’s graphs are based entirely on Euler’s design – with land masses now referred to as a “node” (or “vertex”), while the bridges are the “links” (also known as ‘relationships” and “edges”). With graph databases, end users do not need to know anything about graph theory to experience immediate practical benefits.
Graphs are a vital part of our online lives, powering everything from social media sites – including Twitter and Facebook – to the retail recommendations on eBay. Online dating also owes much of its success to the way graphs can analyze even the most complex relationships, looking not only at location and personal details, but also passions, hobbies, and attitudes, and relationships between all of those things, to identify potential matches. In addition, enterprise efforts in fraud detection, master data management, and network and IT operations are vastly improving thanks to relationship-based insights rooted in graph database usage.
Interest in the graph will continue to grow. The real-time nature of a graph database makes it an excellent platform for unlocking business value from data relationships that simply can’t be identified using traditional SQL or most NoSQL databases. The uses and applications for graph databases seem endless, and it’s exciting to consider what innovations they will continue to power as the world unlocks the value of data relationships.
Emil Eifrem is CEO of Neo Technology and co-founder of Neo4j, the world’s leading graph database. Before founding Neo, he was the CTO of Windh AB, where he headed the development of highly complex information architectures for Enterprise Content Management Systems. Committed to sustainable open source, he guides Neo along a balanced path between free availability and commercial reliability. Emil is a frequent conference speaker and author on NoSQL databases, and tweets at @emileifrem.
With the promise of every major technology trend (big data, analytics, cloud, etc.), there’s always a wave of products and solutions to deliver on said promise. But the one glaring omission from this list is the Internet of Things (IoT). While there are already connected devices and “wearables” out on the market, we have yet to see a proliferation of solutions that are creating an infrastructure revolution in the same vein as say, cloud computing.
I’m reminded of this blog post from Claus Hetting, one of the Wi-Fi industry’s most influential thought leaders, in which he discusses the fact that there are no real-world examples of the “smart cities” that were promised by the IoT – or, more likely, by marketing departments discussing the IoT. We have heard of nothing of this supposed “IoT revolution” and, in my opinion, that’s not surprising.
The Reality of the IoT
It’s my belief that the design of the IoT means the true innovations and game-changing events won’t be the kind of things that grab attention or make headlines. The IoT revolution will happen in the background, outside of the news cycle and invisible to the public eye. This will be the case because the IoT will have to rely on invisible, seamless connections to function. Because of the intricacies of an IoT network and the need for instantaneous connection, the vast majority of the devices that will be making the IoT feasible will be computer chips without an interface that makes it accessible to a user, often referred to as a “headless” device. These won’t just be “smart” refrigerators and coffee makers, but industrial machines, medical equipment, cloud provider data centers, etc.
The Need for Connectivity
These headless devices will bring challenges of their own, and perhaps the biggest of those challenges will be connectivity. The devices will need to sense a network and automatically connect to that network without having a human involved. Right now, telcos such as AT&T insist that cellular networks will be the way to go. But this connectivity quickly will become too much for cellular networks to handle as millions of devices come online. Furthermore, cellular is expensive compared with other connectivity methods. Imagine if every wearable required a monthly cellular connectivity bill! And while enhanced cellular standards such as LTE and 5G are growing in availability, that growth is not nearly as quick as the growth of Wi-Fi hotspot availability. According to Republic Wireless, it’s estimated that the number of global Wi-Fi hotspots will reach 340 million by 2018, which is one hotspot for every 20 people.
The biggest selling point of Wi-Fi over cellular for IoT is the fact that Wi-Fi is a global standard. Users can connect to Wi-Fi via any device in any country where Wi-Fi enabled. Meanwhile, cellular standards change from country to country and only bring connection with exorbitant roaming charges or SIM card changes. Using Wi-Fi, manufacturers could market their IoT products globally without having to change specifications.
Already, the Wi-Fi industry is anticipating this need. In fact, the Wi-Fi Alliance, the non-profit industry association of companies enabling Wi-Fi, created a new class of membership for companies that are creating connected devices. By my estimates, one day we will see 25 billion low-power, low-cost devices permeate our day-to-day activities and deliver immediate benefits for operators, device manufacturers, and consumers.
But what will it take to make this happen? The missing link is technology and a Wi-Fi platform that actually strings these disparate hotspots together. Organizations in all industries will need ubiquitous access to a network that’s large enough and cost-effective enough to work at a global scale. And the underlying technology will require the devices to sense and connect themselves automatically to the network.
For this quiet revolution to take place, there is one final step. The IoT will happen as analysts and technologists envision it only when it can deliver both enterprise and consumer value, as opposed to novelty. What spurred the cloud and big data revolutions was that the enterprise ROI of both was immediately apparent. When enterprises, and not only consumers, can access data, insights, and ROI from connected devices, this quiet revolution will pick up speed. And while we won’t notice it when it happens, we will notice the results.
Gary A. Griffiths is president and Chief Executive Officer of iPass Inc. Griffiths’ initiative is to drive Wi-Fi adoption in iPass’ Open Mobile applications to accelerate the company’s growth. Previously a member of iPass’ Board of Directors, Griffiths was also co-founder and CEO of Trapit, Inc., a leading provider of SaaS-based applications for sales and marketing automation.
A 35-year veteran of the high-tech industry, Griffiths has led some of the Web’s most innovative companies. He has held leadership positions at a number of large and prominent software companies. Prior to founding Trapit, he was president of products and operations at WebEx, acquired for $3B by Cisco Systems, Inc. Griffiths also was co-founder and Chief Executive Officer at Everdream Corporation, a SaaS company, from 1999 to 2005, as well as Chief Executive Officer at HEAT.net from 1996 until its acquisition by Sega, Inc. in 1999.
In addition, Griffiths has extensive experience with overseeing financial and accounting matters and strategic initiatives at small technology companies. Griffiths currently serves on the board of directors of Silicon Graphics International Corp. (NASDAQ: SGI), and Janrain, Inc., a customer identity management SaaS company.
Griffiths holds a bachelor’s degree in aerospace engineering from the U.S. Naval Academy, as well as a Master of Science in business administration from the George Washington University School of Business.
– See more at: http://data-informed.com/the-quiet-revolution-of-the-internet-of-things/#sthash.1ST0zl9o.dpuf
Obtaining real-time data and its analysis in modern times usually refer to countless consumer feedbacks and their interpretation to upgrade the business. But does big data limit itself to consumer interactions? What about the internal functions of the business industries? Check out the big picture of big data analytics on how it actually works for an entire industry or business inside out!
Human resource management
Employees are an intricate part of any business as much are their consumers. Workforce management and constant monitoring of their job and progress ensure the staff are well-trained and updated with the current trends.
HR management utilizes constant data entries of employees’ attendance, leaves, training, achievements, and upgrades to resolve any persisting issues or promote them for the best. It helps the organization step up with a dynamic workforce to develop robust strategies.
Hiring and recruitment of new employees
Businesses or global organizations now depend on online recruitment platforms to search for potential candidates, post their job openings and conduct preliminary tests to screen the candidates. Such platforms like LinkedIn or Indeed have stored records and CVs of thousands of candidates worldwide to list out the favorable ones for the job.
They use artificial intelligence and particular keywords to filter the suitable candidates among all applicants. Employers can set their target words and recruitment steps to customize their hiring channels. Since these are globally connecting platforms with various background checking and filtering steps, they cut the efforts to advertise the jobs and further conduct manual interviews for one and all.
Resource management and allocation
Apart from the records of the customer reactions and feedbacks, the businesses or service providers must also keep track of the allocation of internal resources and raw goods purchased from the suppliers. Big data management tools can efficiently maintain a real-time ledger for noting the available supplies, which department gets what, and which resources are in need.
The tools can categorize and store the data in tables for easy interpretation and quick access for any date and time. They can also have AI and machine learning alarms to notify the dwindling resources for placing fresh orders. Owners can note and mark how much and where are all the resources distributed to crosscheck their effective utilization.
Supply chain management
Manufacturers don’t cut ties after the product is done packing. The shipping services, transportation, freight storage, and customer delivery; all play a major role in supply chain logistics connecting the businesses and their customers.
Big data plays an important role to track and keep a sharp eye on all these stages for efficient functioning throughout the process. Sensors, navigation GPS, warehouse entries, and customer payments; all supply the dynamic data to monitor the entire chain.
Monitoring audit and finance
Money and revenue are the essential factors for analyzing the company’s profit or loss. Timely noting about the payments cleared, taxes paid and the revenues generated helps update the records without missing a penny. A proper analysis helps the owners invest better, saves the businesses from unknown losses, and eases out the money management records.