funded with support from the European Commission. The author is solely responsible for this publication (communication) and the Commission accepts no responsibility for any use that may be made of the information contained therein. The objective of this module is to take a look into what big data can bring you in the future. Upon completion of this module you will: - See what are the predictions for the future of Big Data - Take a look at some trends that are emerging - Get an overview of possible opportunites your company can have with Big Data - Face some of the start up challenges you might have with Big Data Duration of the module: approximately 1 – 2 hours Module 6: The Future of Big and Smart Data
www.smartdata.how This programme has been funded with support from the European Commission. The author is solely responsible for this publication (communication) and the Commission accepts no responsibility for any use that may be made of the information contained therein. 4 Challenges Predictions
that big data has taken the business world by storm, but what’s next? Will data continue to grow? What technologies will develop around it? Will big data become a relic as quickly as the next trend — cognitive technology? Here are some big data predictions from the foremost experts in the field. Smart Data Smart Region | www.smartdata.how
DATA 1 One of the hottest technology trends today is machine learning and it will play a big part in the future of big data as well. It will help businesses in preparing data and conduct predictive analysis so that businesses can overcome future challenges easily. Smart Data Smart Region | www.smartdata.how
the internet of things or big data, the biggest challenge for emerging technologies has been security and privacy of data. The volume of data we are creating right now and the volume of data that will be created in the future will make privacy even more important as stakes will be much higher. Data security and privacy concerns will be the biggest hurdle for big data industry and if it fails to cope with it in an effective manner, we will see a long list of technology trends that became a fad very quickly. Smart Data Smart Region | www.smartdata.how
might be familiar with Chief Executive Officer (CEO), Chief Marketing Officer (CMO) and Chief Information Officer (CIO) but have you ever heard about Chief Data Officer (CDO)? According to Forrester, we will see the emergence of chief data officer as the new position and businesses will appoint chief data officers. Although, the appointment of chief data officer solely depend on the type of business and its data needs but the wider adoption of big data technologies across enterprises, hiring a chief data officer will become the norm. Smart Data Smart Region | www.smartdata.how
volume of data grows and big data grows bigger, demand for data scientists, analysts and data management experts will shoot up. The gap between the demand for data professionals and the availability will widen. This will help data scientists and analysts draw higher salaries. Smart Data Smart Region | www.smartdata.how
see a 360-degree shift in business approach towards software. More and more businesses will look to purchase algorithm instead of creating their own. After buying an algorithm, businesses can add their own data to it. It provides businesses with more customization options as compared to when they are buying software. You cannot tweak software according to your needs. In fact, it is the other way around. Your business will have to adjust according to the software processes but all this will end soon with algorithms selling services taking center stage. Smart Data Smart Region | www.smartdata.how
IDC analysts, “Total revenues from big data and business analytics will rise from $122 billion in 2015 to $187 billion in 2019.” Business spending on big data will surpass $57 billion dollars this year. Although, the business investments in big data might vary from industry to industry, the increase in big data spending will remain consistent overall. Manufacturing industry will spend the most on big data technology while health care, banking, and resource industries will be the fastest to adopt. Smart Data Smart Region | www.smartdata.how
to statistics, there are six million developers currently working with big data and using advanced analytics. This makes up more than 33% of developers in the world. What’s even more amazing is that big data is just getting starting so will see a surge in a number of developer developing applications for big data in years to come. With the financial rewards in terms of higher salaries involved, developers will love to create applications that can play around with big data. Smart Data Smart Region | www.smartdata.how
8 Today, businesses demand single software that provides all the features they need and software companies and giving them that. Business intelligence software is also following that trend and we will see prescriptive analysis capabilities added to this software in the future. IDC predicts that half of the business analytics software will incorporate prescriptive analytics build on cognitive computing functionality. This will help businesses to make intelligent decisions at the right time. With intelligence built into the software, you can sift through large amounts of data quickly and get a competitive advantage over your competitors. Smart Data Smart Region | www.smartdata.how
of your future investments will deliver a higher return on your investment than if you invest in big data, especially when it comes to boosting your business productivity. To give you a better idea, let us put numbers into perspective. According to IDC, organizations that invest in this technology and attain capabilities to analyze large amounts of data quickly and extract actionable information can get an extra $430 billion in terms of productivity benefits over their competitors. Yes, you read that right, $430 billion dollars. Remember, actionable is the key word here. You need actionable information to take your productivity to new heights. Smart Data Smart Region | www.smartdata.how
10 According to some big data experts, big data is dead. They argue that businesses do not even use a small portion of data they have access to and big does not always mean better. Sooner rather than later, big data will be replaced by fast and actionable data, which will help businesses, take the right decisions at the right time. Having tremendous amounts of data will not give you a competitive advantage over your competitors but how effectively and quickly you analyze the data and extract actionable information from it will. Smart Data Smart Region | www.smartdata.how
to monitor the daily shifts in the wind – the minute you sense a direction, it changes. Yet the following trends are clearly shaping Big Data going forward. Smart Data Smart Region | www.smartdata.how
Hadoop, Spark and others have come to dominate the big data space, and that trend looks likely to continue. One survey found that nearly 60 percent of enterprises expect to have Hadoop clusters running in production by the end of this year. And according to Forrester, Hadoop usage is increasing 32.9 percent per year. Experts say that many enterprises will expand their use of Hadoop and NoSQL technologies, as well as looking for ways to speed up their big data processing. Many will be seeking technologies that allow them to access and respond to data in real time. Hadoop is a high profile example of an open source Big Data project. Smart Data Smart Region | www.smartdata.how
in an attempt to speed their big data processing is in-memory technology. In a traditional database, the data is stored in storage systems equipped with hard drives or solid state drives (SSDs). In-memory technology stores the data in RAM instead, which is many, many times faster. A report from Forrester Research forecasts that in-memory data fabric will grow 29.2 percent per year. Several different vendors offer in-memory database technology, notably SAP, IBM, Pivotal. Smart Data Smart Region | www.smartdata.how
enterprises have begun investing in machine learning (ML). Machine learning is a branch of artificial intelligence that focuses on allowing computers to learn new things without being explicitly programmed. In other words, it analyzes existing big data stores to come to conclusions which change how the application behaves. According to Gartner machine learning is one of the top 10 strategic technology trends. It noted that today's most advanced machine learning and artificial intelligence systems are moving "beyond traditional rule-based algorithms to create systems that understand, learn, predict, adapt and potentially operate autonomously." Machine Learning Process Smart Data Smart Region | www.smartdata.how
in fact, ML systems often provide the engines for predictive analytics software. In the early days of big data analytics, organizations were looking back at their data to see what happened and then later they started using their analytics tools to investigate why those things happened. Predictive analytics goes one step further, using the big data analysis to predict what will happen in the future. The number of organizations using predictive analytics today is surprisingly low— only 29 percent according to a 2016 survey from PwC. However, numerous vendors have recently come out with predictive analytics tools, so that number could skyrocket in the coming years as businesses become more aware of this powerful tool. The process of Predictive Analytics Smart Data Smart Region | www.smartdata.how
machine learning and AI technologies is to create intelligent apps. These applications often incorporate big data analytics, analyzing users' previous behaviors in order to provide personalization and better service. One example that has become very familiar is the recommendation engines that now power many ecommerce and entertainment apps. In its list of Top 10 Strategic Technology Trends, Gartner listed intelligent apps second. "Over the next 10 years, virtually every app, application and service will incorporate some level of AI," said David Cearley, vice president and Gartner Fellow. "This will form a long-term trend that will continually evolve and expand the application of AI and machine learning for apps and services." Smart Data Smart Region | www.smartdata.how
into their security strategy. Organizations' security log data provides a treasure trove of information about past cyberattack attempts that organizations can use to predict, prevent and mitigate future attempts. As a result, some organizations are integrating their security information and event management (SIEM) software with big data platforms like Hadoop. Others are turning to security vendors whose products incorporate big data analytics capabilities. Smart Data Smart Region | www.smartdata.how
likely to have a sizable impact on big data. According to a report from IDC, "31.4 percent of organizations surveyed have launched IoT solutions, with an additional 43 percent looking to deploy in the next 12 months." With all those new devices and applications coming online, organizations are going to experience even faster data growth than they have experienced in the past. Many will need new technologies and systems in order to be able to handle and make sense of the flood of big data coming from their IoT deployments. Growth of the Internet of Things Smart Data Smart Region | www.smartdata.how
with their IoT big data is edge computing. In edge computing, the big data analysis happens very close to the IoT devices and sensors instead of in a data center or the cloud. For enterprises, this offers some significant benefits. They have less data flowing over their networks, which can improve performance and save on cloud computing costs. It allows organizations to delete IoT data that is only valuable for a limited amount of time, reducing storage and infrastructure costs. Edge computing can also speed up the analysis process, allowing decision makers to take action on insights faster than before. Edge computing is a new network functionality that offers connected compute and storage resources right next to you Smart Data Smart Region | www.smartdata.how
analytics will likely mean high demand and high salaries for those with big data skills. According to IDC, "In the U.S. alone there will be 181,000 deep analytics roles in 2018 and five times that many positions requiring related skills in data management and interpretation.„ As a result of that scarcity, Robert Half Technology predicts that average compensation for data scientists will increase 6.5 percent in 2017 and range from $116,000 to $163,500. Similarly, big data engineers should see pay increases of 5.8 percent with salaries ranging from $135,000 to $196,000 for next year. Smart Data Smart Region | www.smartdata.how
organizations are likely to be looking for tools that allow regular business professionals to meet their own big data analytics needs. IDC has previously predicted "Visual data discovery tools will be growing 2.5 times faster than rest of the business intelligence (BI) market. By 2018, investing in this enabler of end-user self service will become a requirement for all enterprises." Several vendors have already launched big data analytics tools with "self-service" capabilities, and experts expect that trend to continue into 2017 and beyond. IT is likely to become less involved in the process as big data analytics becomes more integrated into the ways that people in all parts of the business do their jobs. Smart Data Smart Region | www.smartdata.how
1000 company, a 10% increase in usability of and accessibility to data means significant boosts in productivity and sales Smart Data Smart Region | www.smartdata.how
THOSE NUMBERS? To be effective you must be able to discuss the industry-specific needs and pain points of business leaders. GOVERNMENT • Cut costs, improve efficiencies • Improve security, transparency, public participation and internal collaboration • Analyze and predict events related to security, reduce fraud TELECOMMUNICATIONS • Manage high volumes of customer data being driven by operational systems • Deliver value and services by having „single view“ of customer and their changing behavior • Optimize mobile data and network efficiency BANKING • Manage risk and detect fraud • Manage explosive growth in trade volumes and shrinking trade size • Increase customer focus for the business • Reduce data management costs INSURANCE • Improve processing speed of new applications • Reduce inconsistencies in the increased manual claims processing • Customize sales campaigns by improving claims segmentation RETAIL • Manage proliferation of text and numerical data including customer data and transaction information • Optimize marketing spend, increase ROI • Optimize inventory and supply chain MEDICAL • Consolidate data and data center • Automate patient records and vendor payments • Implement electronic health records • Innovate – study the human genome MANUFACTURING • Optimize supply chain • Synchronize data with suppliers for sources products and retailers for sales • Create centralized view of product and parts data for inventory control • Reduce production downtime UTILITIES • Forecast/plan shutdowns • Improve utilization of assets, reduce outages • Improve integration of energy management systems
marketplace is defined by the following segments. STORAGE FOR DATA • This is primarily hardware, and even though Big Data uses less expensive hardware, it uses a lot of it. There are opportunities in developing supercomputing platforms. SERVERS FOR DATABASES • These are the high-end servers and the licensing fees with the supporting consulting. This probably will see changes due to the impact of open source licensing. BUSINESS INTELLIGENCE • BI is the marketplace for traditional data warehousing. This segment will also see a lot of changes. The more traditional solutions will be replaced by supercomputing platforms. But the number might very well increase as former BI solutions migrate to become the backbone technology for many global companies. ADVANCED ANALYTICS • This small market segment will increase and for those who seek this opportunity it could be a real moneymaker. Companies will also be very willing to spend resources in terms of consulting and training. DATA INTEGRATION • There's a lot of stranded data to be rescued and these are tough jobs with a lot of challenges. There will be a lot of new software tools and a lot of small niche companies emerging in this space. TEXT ANALYTICS • Another small segment which may come with some interesting surprises. There are a handful of very specialty companies out there, but any one of them could bring forward a remarkable solution with universal appeal.
stall in different areas. These areas can range from deciding exactly what to do with the data to deciding how to provide more people with more access to data. We have touched some of the big data challenges already in the Module 1, now let‘s take a closer look at the challenges you might face business wise when you dive into big data. Smart Data Smart Region | www.smartdata.how
Your Big Data Use Cases Why It’s a Challenge If there’s one issue that repeats itself with new big data users, it’s the importance of determining the right business use cases. If you’re trying to prove the value of your program (and at some point, you’re going to have to), you need to start with some solid use cases in mind. The problem is selecting the right use case. There are dozens, hundreds of use cases out there. But it’s best if you choose one where you can not only analyze data to find meaningful trends, but also work with the business teams to make an impact using your data. Your data is going to have to prove its business value. What Can You Do? There are many online tools like (e. g. Use Case Browser) with hundreds of real-life use cases. You can filter through results to find ones that are suitable for your purposes. With all this emphasis on how important it is to select a business use case, you might find yourself stressing about picking the perfect one. You shouldn‘t stress yourself out too much, but just pick out a few smaller use cases first. Smaller use cases mean it will also be faster to gain results and start demonstrating impact. This will give you a morale boost and some quick wins to provide motivation as you begin your big data journey.
Agility to Get Answers Fast Why It’s a Challenge Organizations want to find answers fast so they can increase the speed at which they do business. Your agility will come from addressing five key challenges with big data analytics: – Effective data management, with efficient management and retention of the right data to optimize storage and flow – Dealing with data complexity and inaccuracy, with an effective curation process to tame the data and make it useful – Enabling free-form discovery, with a self-service, data-first approach to exploration and discovery – Controlling data without stifling innovation, with easily moderated access that keeps private data locked down – Getting results to the business, which requires continuously running processes that feed data to the business What Can You Do? Building a data lake does provide a single repository of your organization’s data, whether it’s structured, unstructured, internal or external. This allows your business analysts and data scientists to potentially mine all of your organization’s data.
Governance Around Your Big Data Why It’s a Challenge Part of agility around your data will come with good governance, which will allos you to share data while controlling access. At its best, data governance doesn’t just establish a defense around your data. It also creates an environment that makes data trustworthy, easily discoverable and highly available to the right people. Data governance is always important. But because of the workflow around a data lake and the volume of data in play, governance is even more important in the realm of big data. What Can You Do? Developing a successful data governance strategy requires a great deal of effort—careful planning, the right people and the right tools.
Your Big Data Journey Why It’s a Challenge Big data isn’t simply a project. It’s a journey and we want you to be successful. But many companies have stalled in their big data journeys. The majority of the time, technology is not the issue; it’s very possible to become successful at big data. However, a successful big data journey requires a commitment to cultural changes, business model adjustments, new process and additional skills. That’s the difficult part. What Can You Do? There’s a lot that goes into a successful big data project. You’ll have to take into account the complexity of your data, the complexity of your analytics and what you want your data journey to look like. Decide where you currently are in your data journey. Here’s how we classify them: – Ad-hoc – The earliest phase, where organizations experiment with and learn about their big data needs. – Opportunistic – The second phase when an organization starts to deliver value to the business, building their skills and knowledge. – Repeatable – The organization will start to deliver value to the business, building their skills and knowledge. – Managed – The big data analytics becomes a managed service that starts to spread across the organization. – Optimized – The big data analytics becomes a well-oiled machine, continuously delivering new projects and exponential value.
Consider With Big Data Analytics Software? Why It’s a Challenge Part of efficient big data analytics is selecting the right platform to help you through it. But what should you look for? And do you want to build your solution or buy it? Or bridge an available software with what you have in-house? What Can You Do? Start researching. There really isn’t a short answer to this, unfortunately. Most of the time, you’ll find that a hybrid approach where you build some and buy some works best for delivering a complete view of the business.
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