The amount of data produced by humans has exploded to unheard-of levels, with nearly 2.5 quintillion bytes of data created daily. With advances in the Internet of Things and mobile technology, data has become a central interest for most organizations. More importantly than simply collecting it, though, is the real need to properly analyze and interpret the data that is being gathered. Also, most businesses collect data from a variety of sources, and each data stream provides signals that ideally come together to form useful insights. However, getting the most out of your data depends on having the right tools to clean it, prepare it, merge it and analyze it properly.
Here are ten of the best analytics tools your company can take advantage of in 2019, so you can get the most value possible from the data you gather.
What is Big Data?
Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Furthermore, Big Data is nothing but any data which is very big to process and produce insights from it. Also, data being too large does not necessarily mean in terms of size only. There are 3 V’s (Volume, Velocity and Veracity) which mostly qualifies any data as Big Data. The volume deals with those terabytes and petabytes of data which is too large to process quickly. Velocity deals with data moving with high velocity. Continuous streaming data is an example of data with velocity and when data is streaming at a very fast rate may be like 10000 of messages in 1 microsecond. Veracity deals with both structured and unstructured data. Data that is unstructured or time-sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Trending Big Data Tools in 2019
1. Apache Spark
Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.
Spark is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries and streaming. Apart from supporting all these workloads in a respective system, it reduces the management burden of maintaining separate tools.
Apache Spark has the following features.
Speed − Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk. This is possible by reducing the number of reading/write operations to disk. It stores the intermediate processing data in memory.
Supports Multiple languages − Spark provides built-in APIs in Java, Scala, or Python. Therefore, you can write applications in different languages. Spark comes up with 80 high-level operators for interactive querying.
Advanced Analytics − Spark not only supports ‘Map’ and ‘reduce’. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph Algorithms.
2. Apache Kafka
Apache Kafka is a community distributed event streaming platform capable of handling trillions of events a day. Initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log. Since being created and open sourced by LinkedIn in 2011, Kafka has quickly evolved from messaging queue to a full-fledged event streaming platform.
Following are a few benefits of Kafka −
Reliability − Kafka is distributed, partitioned, replicated and fault tolerance
Scalability − Kafka messaging system scales easily without downtime
Durability − Kafka uses Distributed commit log which means messages persists on disk as fast as possible, hence it is durable
Performance − Kafka has high throughput for both publishing and subscribing messages. It maintains stable performance even many TB of messages are stored.
Kafka is very fast and guarantees zero downtime and zero data loss.
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
It provides a high-throughput, low-latency streaming engine as well as support for event-time processing and state management. Flink applications are fault-tolerant in the event of machine failure and support exactly-once semantics. Programs can be written in Java, Scala, Python and SQL and are automatically compiled and optimized into dataflow programs that are executed in a cluster or cloud environment. Flink does not provide its own data storage system, but provides data source and sink connectors to systems such as Amazon Kinesis, Apache Kafka, Alluxio, HDFS, Apache Cassandra, and ElasticSearch.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Following are the few advantages of using Hadoop:
Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores
Hadoop does not rely on hardware to provide fault-tolerance and high availability
You can add or remove the cluster dynamically and Hadoop continues to operate without interruption
Another big advantage of Hadoop is that apart from being open source, it is compatible with all the platforms
The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. Cassandra’s support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
Cassandra has become so popular because of its outstanding technical features. Given below are some of the features of Cassandra:
Elastic Scalability — Cassandra is highly scalable; it allows to add more hardware to accommodate more customers and more data as per requirement
Always on Architecture — Cassandra has no single point of failure and it is continuously available for business-critical applications that cannot afford a failure
Fast linear-scale Performance — Cassandra is linearly scalable, i.e., it increases your throughput as you increase the number of nodes in the cluster. Therefore it maintains a quick response time
Flexible Data Storage — Cassandra accommodates all possible data formats including: structured, semi-structured, and unstructured. It can dynamically accommodate changes to your data structures according to your need
Easy Data Distribution — Cassandra provides the flexibility to distribute data where you need by replicating data across multiple data centers
Transaction Support — Cassandra supports properties like Atomicity, Consistency, Isolation, and Durability (ACID)
Fast Writes — Cassandra was designed to run on cheap commodity hardware. It performs blazingly fast writes and can store hundreds of terabytes of data, without sacrificing the read efficiency
6. Apache Storm
Apache Storm is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. The storm is simple, can be used with any programming language, and is a lot of fun to use!
It has many use cases: real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. The storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant guarantees your data will be processed, and is easy to set up and operate.
RapidMiner is a data science software platform by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.
8. Graph Databases (Neo4J and GraphX)
Graph databases are NoSQL databases which use the graph data model comprised of vertices, which is an entity such as a person, place, object or relevant piece of data and edges, which represent the relationship between two nodes.
They are particularly helpful because they highlight the links and relationships between relevant data similarly to how we do so ourselves.
Even though graph databases are awesome, they’re not enough on their own.
Advanced second-generation NoSQL products like OrientDB, Neo4j are the future. The modern multi-model database provides more functionality and flexibility while being powerful enough to replace traditional DBMSs.
9. Elastic Search
Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.
Following are advantages of using elastic search:
Elasticsearch is over Java, which makes it compatible on almost every platform.
It is real time, in other words, after one second the added document is searchable in this engine.
Also, it is distributed, which makes it easy to scale and integrate into any big organization.
Creating full backups are easy by using the concept of the gateway, which is present in Elasticsearch.
Handling multi-tenancy is very easy in Elasticsearch
Elasticsearch uses JSON objects as responses, which makes it possible to invoke the Elasticsearch server with a large number of different programming languages.
Elasticsearch supports almost every document type except those that do not support text rendering.
Exploring and analyzing big data translates information into insight. However, the massive scale, growth and variety of data are simply too much for traditional databases to handle. For this reason, businesses are turning towards technologies such as Hadoop, Spark and NoSQL databases to meet their rapidly evolving data needs. Tableau works closely with the leaders in this space to support any platform that our customers choose. Tableau lets you find that value in your company’s data and existing investments in those technologies so that your company gets the most out of its data. From manufacturing to marketing, finance to aviation– Tableau helps businesses see and understand Big Data.
Understanding your company’s data is a vital concern. Deploying any of the tools listed above can position your business for long-term success by focusing on areas of achievement and improvement.
Follow this link, if you are looking to learn more about data science online!
Artificial intelligence uses data science and algorithms to automate, optimize and find value hidden from the human eye. By one estimate, artificial intelligence will drive nearly $2 trillion worth of business value worldwide in 2019 alone. Hence, that’s an excellent incentive to grab a slice of the AI bounty. Also, fortune favors those who get an early start. Therefore, the laggards might not be so fortunate.
Artificial Intelligence (AI) is the rage now, but like all things tech, it is in a continuous state of evolution. Here is how Artificial Intelligence is expected to play out in 2019.
Trends in Artificial Intelligence
1. Automation of DevOps to achieve AIOps
There’s been a lot of attention in recent years about how artificial intelligence (AI) and machine learning (ML). Furthermore, DevOps is all about automation of tasks. Its focus is on automating and monitoring steps in the software delivery process, ensuring that work gets done quickly. AI and ML are perfect fits for a DevOps culture. Also, they can process vast amounts of information and help perform menial tasks. They can learn patterns, anticipate problems and suggest solutions. If DevOps’ goal is to unify development and operations, AI and ML can smooth out some of the tensions that separate the two disciplines in the past.
Moreover, one of the key tenets of DevOps is the use of continuous feedback loops at every stage of the process. This includes using monitoring tools to provide feedback on the operational performance of running applications. Additionally, this is one area today where ML is impacting DevOps already. Also, using automation technology, chatbots, and other AI systems, these communications channels can become more streamlined and proactive. Also, in the future, we can see AI/ML’s application in other stages of the software development life cycle. This will provide enhancements to a DevOps methodology or approach.
Furthermore, one area where this may happen could be in the area of software testing. Unit tests, regression tests, and other tests all produce large amounts of data in the form of test results. Applying AI to these test results could identify patterns of poor codes resulting in errors caught by the tests.
2. The Emergence of More Machine Learning Platforms
People are yet not done figuring out machine learning, and now there is a rise of a new advanced term on the market for machine learning, and, i.e. “Automated Machine Learning.” Automated machine learning is a more straightforward concept, and it makes things easier for developers and professionals. Furthermore, AutoML is a shift from traditional rule-based programming to an automation form where machines can learn the rules. Also, in automated machine learning, we offer a relevant and diverse set of reliable data to, in the beginning, to help automate the process of decision making. The engineers will no longer have to spend time on repetitive tasks, thanks to AutoML. The growth in the demand for machine learning professionals will get a massive boost with the rise of AutoML.
We’re in a golden era where all platform mega-vendors providing mobile infrastructure are rolling out mobile-accessible tools for mobile developers. For example:
Imagine a world where you can sit next to your customers and have a one on one conversation about their expectations from your brand with every interaction, and deliver on their expectations every single time. As we move forward in the digital era, this might be the reality for the brands, where businesses get the opportunity to win their customers’ heart with every single interaction. Artificial Intelligence and Augmented Reality are two such technologies, which will show the most potential in connecting with consumers in 2019 and will rule the technology landscape. The key reason behind this trend is that, compared to virtual reality, which needs a hardware device like Oculus Rift, it is fairly simple to implement augmented reality. It only needs a smartphone and an app.
Since the entry barrier is low, today’s tech-savvy consumers do not shy away from experimenting with the technology, and for enterprises, it only requires a thought-through AR-based app. Further, with tech giants like Apple, Google, Facebook making it easier for developers to build AR-based apps for their platforms, it has become easier even for smaller businesses to invest in augmented reality. Also, industries like retail, healthcare, travel etc. have already created a lot of exciting use cases with AR. With technology giants Apple, Google, Facebook etc. offering tools to make the development of AR-based apps easier, 2019 will see an upsurge in the number of AR apps being released.
4. Agent-Based Simulations
Agent-based modelling is a powerful simulation modelling technique that has seen a number of applications in the last few years, including applications to real-world business problems. Furthermore, in agent-based modelling (ABM), a system is modelled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviours appropriate for the system they represent — for example, producing, consuming, or selling.
The benefits of ABM over other modelling techniques can be captured in three statements: (i) ABM captures emergent phenomena; (ii) ABM provides a natural description of a system; and (iii) ABM is flexible. It is clear, however, that the ability of ABM to deal with emergent phenomena is what drives the other benefits.
Also, ABM uses a “bottom-up” approach, creating emergent behaviours of an intelligent system through “actors” rather than “factors”. However, macro-level factors have a direct impact on macro behaviours of the system. Macy and Willer (2002) suggest that bringing those macro-level factors back will make agent-based modelling more effective, especially in intelligent systems such as social organizations.
The Internet of Things is reshaping life as we know it from the home to the office and beyond. IoT products grant us extended control over appliances, lights, and door locks. They also help streamline business processes; and more thoroughly connect us to the people, systems, and environments that shape our lives. IoT and data remain intrinsically linked together. Data consumed and produced keeps growing at an ever expanding rate. This influx of data is fueling widespread IoT adoption as there will be nearly 30.73 billion IoT connected devices by 2020.
Data Analytics has a significant role to play in the growth and success of IoT applications and investments. Analytics tools will allow the business units to make effective use of their datasets as explained in the points listed below.
Volume: There are huge clusters of data sets that IoT applications make use of. The business organizations need to manage these large volumes of data and need to analyze the same for extracting relevant patterns. These datasets along with real-time data can be analyzed easily and efficiently with data analytics software.
Structure: IoT applications involve data sets that may have a varied structure as unstructured, semi-structured and structured data sets. There may also be a significant difference in data formats and types. Data analytics will allow the business executive to analyze all of these varying sets of data using automated tools and software.
Driving Revenue: The use of data analytics in IoT investments will allow the business units to gain insight into customer preferences and choices. This would lead to the development of services and offers as per the customer demands and expectations. This, in turn, will improve the revenues and profits earned by the organizations.
6. AI Optimized Hardware
The demand for artificial intelligence will increase tremendously in the next couple of years, and it’s no surprise considering the fact it’s disrupting basically every major industry. Yet as these systems do more and more complex tasks, they demand more computation power from hardware. Machine learning algorithms are also present locally on a variety of edge devices to reduce latency, which is critical for drones and autonomous vehicles. Local deployment also decreases the exchange of information with the cloud which greatly lowers networking costs for IoT devices.
Current hardware, however, is big and uses a lot of energy, which limits the types of devices which can run these algorithms locally. But being the clever humans we are, we’re working on many other chip architectures optimized for machine learning which are more powerful, energy efficient, and smaller.
There’s a ton of companies working on AI specific hardware
Google’s tensor processing units (TPU), which they offer over the cloud and costs just a quarter compared to training a similar model on AWS.
Microsoft is investing in field programmable gate arrays (FGPA) from Intel for training and inference of AI models. FGPA’s are highly configurable, so they can easily be configured and optimized for new AI algorithms.
Intel has a bunch of hardware for specific AI algorithms like CNN’s. They’ve also acquired Nervana, a startup working on AI chips, with a decent software suite for developers as well.
IBM’s doing a lot of research into analogue computation and phase changing memory for AI.
Nvidia’s dominated the machine learning hardware space because of their great GPU’s, and now they’re making them even better for AI applications, for example with their Tesla V100 GPU’s.
7. Natural Language Generation
The global natural language generation market size will grow from USD 322.1 million in 2018 to USD 825.3 million by 2023. A necessity to understand customers’ behaviour has led to a rise in better customer experience across different industry verticals. This factor is driving organisations to build personalised relationships based on customers’ activities or interactions. Moreover, big data created an interest among organisations to derive insights from collected data for taking better and real-time decisions. Thus, NLG solutions have gained significance in extracting insights into human-like languages that are easy to understand. However, the lack of a skilled workforce to deploy NLG solutions is a major factor restraining the growth of the market.
8. Streaming Data Platforms
Streaming data platforms bring together are not only about just low-latency analysis of information. But, the important aspect lies in the ability to integrate data between different sources. Furthermore, there is a rise in the importance of data-driven organizations and the focus on low-latency decision making. Hence, the speed of analytics increased almost as rapidly as the ability to collect information. This is where the world of streaming data platforms comes into play. These modern data management platforms bring the ability to integrate information from operation systems in real-time/near real-time.
Through Streaming analytics, real-time information can be gathered and analyzed from and on the cloud. The information is captured by devices and sensors that are connected to the Internet. Some examples of these streaming platforms can be
Spark Streaming/Structured Streaming
Azure Streaming services
9. Driverless Vehicles
Car manufacturers are hoping autonomous-driving technology will spur a revolution among consumers, igniting sales and repositioning the U.S. as the leader in the automotive industry. Companies like General Motors and Ford are shifting resources away from traditional product lines and — alongside tech companies like Google’s Waymo — pouring billions into the development of self-driving cars. Meanwhile, the industry is pressuring Congress to advance a regulatory framework that gives automakers the confidence to build such vehicles without worrying whether they’ll meet as-yet-unspecified regulations that might bar them from the nation’s highways.
Supporters say the technology holds immense promise in reducing traffic deaths and giving elderly individuals and other population groups access to safe and affordable alternatives to driving themselves. Achieving those benefits, however, will come with trade-offs.
10. Conversational BI and Analytics
We are seeing two major shifts happening in entire BI/Analytics and AI space. First, analytic capabilities are moving toward augmented analytics, which is capable of giving more business down insights and has less dependency on domain experts. Second, we are seeing is the convergence of conversational platforms with these enhanced capabilities around augmented analytics. We expect these capabilities and adoption to quickly proliferate across organizations, especially those organizations that already are having some form for BI in place.
Many technology experts postulate that the future of AI and machine learning is certain. It is where the world is headed. In 2019 and beyond these technologies are going to shore up support as more businesses come to realize the benefits. However, the concerns surrounding the reliability and cybersecurity will continue to be hotly debated. The artificial intelligence trends and machine learning trends for 2019 and beyond hold promises to amplify business growth while drastically shrinking the risks. So, are you ready to take your business to the next level with Artificial Intelligence trends?
Follow this link, if you are looking to learn more about data science online!
As organizations turn to digital transformation strategies, they are also increasingly forming teams around the practice of Data Science. Currently, the main challenge for many CIOs, CDOs, and other Chief Data Scientists consist of positioning the Data Science function precisely where an organization needs it to improve its present and future activities. This implies embedding Data Science teams to fully engage with the business and adapting the operational backbone of the company
Furthermore, with all the requirements and expectations businesses are having from data science, innovation and experimentation will be key factors moving data science forward. Moreover, let us have a look at the growth of data science in recent years. After that, we will understand how creativity and innovation have accelerated this growth till now and the future prospects.
The Growth of Data Science
LinkedIn recently published a report naming the fastest growing jobs in the US based on the site’s data. The social networking site compared data from 2012 and from 2017 to complete the report. Eventually, the top two spots were machine learning jobs, which grew by 9.8X in the past five years, and data scientist, which grew 6.5X since 2012. So why are data science positions, and specifically machine learning positions, growing so fast?
1. The amount of data has skyrocketed Not only has roughly 90 per cent of the data created in the last two years, but the current data output is 2.5 quintillion bytes of data daily
2. Data-driven decisions are more profitable In the end, for many companies, data is not useful unless it is beneficial, which it certainly is. Data not only helps companies make better decisions but those decisions also usually come with a financial gain. Furthermore, a study by Harvard Business Review found that “companies in the top third of their industry in the use of data-driven decision making were more productive and profitable than their competitors.
3. Machine learning is changing how you do business Machine learning is a type of artificial intelligence (AI) where the systems can actually learn and evolve. Also, it has infiltrated many industries, from marketing to finance to health care. The advanced algorithms save time and resources, making quick, correct decisions based on past learnings
4. Machine learning provides better forecasting Machine learning algorithms often find hidden insights that went unseen by the human eye. With the vast amount of data in the processing stage, even an entire team of data scientists might miss a particular trend or pattern. The ability to predict what will happen in the market is what keeps businesses competitive.
Why Creativity and Curiosity are Needed for Growth of Data Science?
Data Science is More About Asking Why?
Data science is focussed on querying every result and having an inquisitive mindset. You can not be a good data scientist if you lack the inquisitive skills. Furthermore, an Inquisitive nature in a data scientist plays a major role in bringing out hidden patterns and insights present in the data. Data can be complex and answer to your hypothesis may lie somewhere hidden in the data. But, It is the inquisitive skills of a data scientist which leverages the hidden potential of data in achieving business goals.
Varied Implementations in Different Domains
Industry influencers, academicians, and other prominent stakeholders certainly agree that data science has become a big game changer in most, if not all, types of modern industries over the last few years. As big data continues to permeate our day-to-day lives, there has been a significant shift of focus from the hype surrounding it to finding real value in its use. Also, data science finds it’s usage in the most unlikely places one can ever think of now. Such varied implementations and decision making require creativity and curiosity in the minds of data scientists.
Different Problems — One Solution
This talks about the idea of dealing with multiple problems at hand with one solution. There can be solutions to different problems, but re-using an old solution from different problem space and applying it in the unlikely domains(extreme experimentation) sure has resulted in some great ideas recently. For example, CNN in deep learning is a classic implementation for image processing. But who could have thought that an image processing algorithm can also give strikingly good results in processing natural language? But, today CNN is also widely used for doing natural language processing. Creativity and curiosity take time to innovate things but when it does, it all worth the time invested!
One Problem — Multiple Solutions
We emphasise more on having multiple solutions for a single problem here. Having multiple ways of solving a given problem requires creativeness in mind. One should be ready to experiment and challenge the existing methods of solving a given problem. Furthermore, innovation can only occur when existing methods are challenged rather than just plainly accepting them. If everyone was to accept earlier beliefs, then maybe we could have been stuck with linear regression forever and will not have algorithms like SVM and Random Forest. Hence, It is this inquisitive nature which actaully gave birth to these classic ML algorithms today we have with us.
Examples of Innovations in Data Science in Recent Years
1. Coca-Cola managed to strengthen its data strategy by building a digital-led loyalty program. Coca-Cola director of data strategy was interviewed by ADMA managing editor. The interview made it clear that big data analytics is strongly behind customer retention at Coca-Cola.
2. Netflix is a good example of a big brand that uses big data analytics for targeted advertising. With over 100 million subscribers, the company collects huge data, which is the key to achieving the industry status Netflix boosts. If you are a subscriber, you are familiar with how they send you suggestions for the next movie you should watch. Basically, this is done using your past search and watch data. This data is used to give them insights on what interests the subscriber most.
3. Amazon leverages big data analytics to move into a large market. The data-driven logistics gives Amazon the required expertise to enable creation and achievement of greater value. Focusing on big data analytics, Amazon whole foods are able to understand how customers buy groceries and how suppliers interact with the grocer. This data gives insights whenever there is a need to implement further changes.
Creative Solutions for Innovation using Data Science
1. Profit model Crunching numbers can identify untapped potential hidden in the profit margins or pin-point insufficiently used revenue streams. Simulations can also show if specific markets are ready. Data can help you apply the 80/20 principle and focus on your top clients.
2. Network Data recorded and analyzed by one company can benefit others in numerous ways, especially if the two entities are in complementary businesses. Just imagine how a hotel could boost their bookings by using the weather and delayed flights information collected by a nearby airport during their regular operations.
3. Structure Algorithms to ingest organizational charts with augmented information from thousands of companies and produce models of the best performing. It could offer recipes for the gender and educational composition of a Board to maximize talent. This could end artificial efforts of having more women on the board and produce even recommendations of possible candidates by scanning professional profiles.
4. Process Data science consulting company InData Labs states that using analytics in the company’s operations is the best way to handle uncertainty by teaching staff to guide their decisions on results and numbers instead of gut feeling and customs.
5. Product performance One company which already does this through their newsfeed automation is Facebook. They have innovated the way it looks for each individual user to boost their revenue from PPC ads. By employing data science in every aspect of user experience,
you can create better products and cut development costs by abandoning bad ideas early on.
How to Encourage Curiosity and Creativity among Data Scientists
1. Give importance to data science in growth planning Don’t bury it under another department like marketing, product, finance, etc. Set up an innovation and development wing for research and experimentation purposes which is separate from business deadlines. The data science team will need to collaborate with other departments to provide solutions. But it will do so as for equal partners, not as a support staff that merely executes on the requirements from other teams. Instead of positioning data science as a supportive team in service to other departments, make it responsible for business goals
2. Provide the required infrastructure Give full access to data as well as the compute resources to process their explorations. Requiring them to ask permission or request resources will impose a cost and less exploration will occur.
3. Focus on learning over knowing Entire company must have common values for things like learning by doing, being comfortable with ambiguity, balancing long-and short-term returns. These values should spread across the entire organisation as they cannot survive in isolation.
4. Laying importance of extreme experimentation More emphasis should be put on experimentation tasks and mindset. Having an experimentation mindset gives the ability to data scientists to take steps into something innovative. Experimentation brings you a step closer to innovation and data science is all about it!
Creativity in data science can be anything from innovative features for modelling, development of new tools, cool new ways to visualise data, or even the types of data that we use for analysis. What’s interesting in data is that everyone will do things differently, depending on how they think about the problem. When put that way, almost everything we do in data science can be creative if we think outside the box a little bit.
The best way I can think to describe creativity in a candidate or in an approach is when they give you this moment of “wow!.” Ideally, as a company or team, you want to have a maximum number of moments like this — keep good ideas flowing, prioritize, and execute.
Follow this link, if you are looking to learn more about data science online!
Data Science is a study which deals with the identification, representation, and extraction of meaningful information from data. It can be collected from different sources to be used for business purposes.
With an enormous amount of facts generating each minute, the requirement to extract the useful insights is a must for the businesses. It helps them stand out from the crowd. Data engineers set up the data storage in order to facilitate the process of data mining, data munging activities. Every other organization is running behind profits. But the companies that formulate effective strategies based on insights always win the game in the long-run.
In this blog, we will be discussing new advancements or trends in the data science industry. Consecutively, these advancements are enabling it to tackle some of the trickiest problems across various businesses.
Top 5 Trends
Analytics and associated data technologies have emerged as core business disruptors in the digital age. As companies began the shift from being data-generating to data-powered organizations in 2017, data and analytics became the centre of gravity for many enterprises. In 2018, these technologies need to start delivering value. Here are the approaches, roles, and concerns that will drive data analytics strategies in the year ahead.
The Data Science Trends for 2018 are largely a continuation of some of the biggest trends of 2017 including Big Data, Artificial Intelligence (AI), Machine Learning (ML), along with some newer technologies like Blockchain, Serverless Computing, Augment Reality, and others that employ various practices and techniques within the Data Science industry.
If I am to pick top 5 data science trends right now (which can be very subjective but I will try it to justify the most), I will list them as
Let us understand each of them in bit more detail!
Artificial intelligence (AI) is not new. It has been around for decades. However, due to greater processing speeds and access to vast amounts of rich data, AI is beginning to take root in our everyday lives.
From natural language generation and voice or image recognition to predictive analytics, machine learning, and driverless cars, AI systems have applications in many areas. These technologies are critical to bringing about innovation, providing new business opportunities and reshaping the way companies operate.
Artificial Intelligence is itself a very broad area to explore and study. But there are some components within artificial intelligence which are making quite a buzz around with their applications across business lines. Let us have a look at them one by one.
Natural language Processing
With advances in computational power and the integration of artificial intelligence, the natural language processing domain has evolved into a whirlwind of innovation. In fact, experts expect the NLP market to swell to an impressive $22.3 billion by 2025. One of the many applications of NLP in business is chatbots. Chatbots demonstrate utility in the customer service realm. These automated helpers can take care of simple frequently asked questions and other lookup tasks. This leaves customer service agents free to devote time to troubleshooting bigger matters that personalize and enhance the customer experience. Chatbots can save valuable time and energy for all members of the value stream. Chatbot technology is poised for considerable growth as speech and language processing tools become more robust by expanding beyond rules-based engines to include neural conversational models.
You might think that Deep Learning sounds a lot like Artificial Intelligence, and that’s true to a point. Artificial Intelligence is a machine developed with the capability for intelligent thinking. Deep Learning, on the other hand, is an approach to Machine Learning which involves Artificial Neural Networks to work with the data. Today, there are more Deep Learning business applications than ever. In different cases, it can be the core offering of the product, such as self-driving cars. Over the past few years. It is found powering some of the world’s most powerful tech today: everything from entertainment media to self-driving cars. Some of the applications of deep learning in business include recommender systems, self-driving cars, image detection, and object classification.
The reinforcement learning model prophesies interaction between two elements — Environment and the learning agent. The learning agent leverages two mechanisms namely exploration and exploitation. When the learning agent acts on trial and error, it is termed as exploration, and when it acts based on the knowledge gained from the environment, it is referred to as exploitation. The environment rewards the agent for corrective actions, which is the reinforcement signal. Leveraging the rewards obtained, the agent improves its environment knowledge to select the next action. Now, artificial agents are being created to perform the tasks as a human. These agents have made their presence felt in businesses, and the use of agents driven by reinforcement learning is cut across industries. Some of the practical applications of reinforcement learning include robots driven in the factory, space management in warehouses, dynamic pricing agents, and driving financial investment decisions.
The complexity in data science is increasing by the day. This complexity is driven by fundamental factors like increased data generation, low-cost storage, and cheap computational power. So, in summary, we are generating far more data, we can store it at a low cost and can run computations and simulations on this data at a low cost!
To tackle the increasing complexities in data science here is why we need cloud services
Need to run scalable data science
The larger ecosystem for machine learning system deployments
Use for building quick prototypes
In the field of cloud services, we have 3 major players in this field leading the pack. AWS(Amazon), Azure(Microsoft), GCP(Google).
Augmented Reality/Virtual Reality Systems
The Immersive Experience related to augmented reality (AR) and virtual reality (VR) is already changing the world around us. The human-machine interaction will improve as research breakthroughs in AR and VR come about. It is a claim made in a Gartner report, Augmented Analytics is the future of Data and Analytics, published in July 2017. Augmented analytics automates data insights through machine learning and natural language processing, enabling analysts to find patterns and prepare smart data that can be easily shared and operationalized. Accessible augmented analytics produces citizen data scientists and make an organization more agile.
Internet of things refers to a network of objects, each of which has a unique IP address & can connect to the internet. These objects can be people, animal and day to day devices like your refrigerator and your coffee machine. These objects can connect to the internet (and to each other) and communicate with each other through this net, in ways which have not been thought before. The data from current IoT pilot rollouts (sensors, smart meters, etc) will be used to make smart decisions using predictive analytics. E.g., forecast electricity usage from each smart meter to better plan distribution; forecast power output of each wind turbine in a wind farm; predictive maintenance of machines, etc.
The power of Big Data
Big data is a term to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with.
It was a significant trend in data science in 2017 but lately, there have been some advancements in Big data lately which has made it a trend in 2018 too. Let us have a look at some of them
Data science is a central part of virtually everything — from business administration to running local and national governments. At its core, the subject aims at harvesting and managing data so organizations can run smoothly. For some time now, data scientists have been unable to share, secure and authenticate data integrity. Thanks to bitcoin being overly hyped, the blockchain, the technology that underpins it, got the attentive eyes of data specialists. Blockchain Improves data integrity, provides easy and trusted means of data sharing capabilities, enable real-time analysis and data traceability. With robust security and transparent record keeping, blockchain is set to help data scientists achieve many milestones that were previously considered impossible. Although the decentralized digital ledgers are still a novice technology, the preliminary results from companies experimenting on them, like IBM and Walmart, prove that they work.
Stream Processing is a Big data technology. It enables users to query continuous data stream and detect conditions fast within a small time period from the time of receiving the data. The detection time period may vary from few milliseconds to minutes. For example, with stream processing, you can receive an alert by querying a data streams coming from a temperature sensor and detecting when the temperature has reached the freezing point. Streaming data possesses immense capabilities which makes it a running trend in Big data till date.
Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs to allow data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets. With Spark running on Apache Hadoop YARN, developers everywhere can now create applications to exploit Spark’s power, derive insights, and enrich their data science workloads within a single, shared dataset in Hadoop. With apache releasing new features time by time to its spark library (Spark streaming, GraphX etc), it has been able to maintain its hold as a trend in Big Data till data
This is only the beginning, as data science continues to serve as the catalyst in the changes you are going to experience in business and technology. It is now up to you on how to efficiently adapt to these changes and help your own business flourish.