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.
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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?
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2019 looks to be the year of using smarter technology in a smarter way. Three key trends — artificial intelligence systems becoming a serious component in enterprise tools, custom hardware breaking out for special use-cases, and a rethink on data science and its utility — will all combine into a common theme.
In recent years, we’ve seen all manner of jaw-dropping technology, but the emphasis has been very much on what these gadgets and systems can do and how they do it, with much less attention paid to why.
In this blog, we will explore different areas in data science and figure out our expectations in 2019 in them. Areas include machine learning, AR/VR systems, edge computing etc. Let us go through them one by one
Machine Learning/Deep Learning
Businesses are using machine learning to improve all sorts of outcomes, from optimizing operational workflows and increasing customer satisfaction to discovering to a new competitive differentiator. But, now all the hype around AI is settling. Machine learning is not a cool term anymore. Furthermore, organisations are looking for more ways of identifying more options in the form of agent modelling. Apart from this, more adoption of these algorithms looks very feasible now. Adoption will be seen in new and old industries
Healthcare companies are already big users of AI, and this trend will continue. According to Accenture, the AI healthcare market might hit $6.6 billion by 2021, and clinical health AI applications can create $150 billion in annual savings for the U.S. healthcare economy by 2026.
In retail, global spending on AI will grow to $7.3 billion a year by 2022, up from $2 billion in 2018, according to Juniper Research. This is because companies will invest heavily in AI tools that will help them differentiate and improve the services they offer customers.
In cybersecurity, the adoption of AI brings a boom in startups that are able to raised$3.65 billion in equity funding in the last five years. Cyber AI can help security experts sort through millions of incidents to identify aberrations, risks, and signals of future threats.
And there is even an opportunity brewing in industries facing labour shortages, such as transportation. At the end of 2017, there was a shortage of 51,000 truck drivers (up from a shortage of 36,000 the previous year). And the ATA reports that the trucking industry will need to hire 900,000 more drivers in the next 10 years to keep up with demand. AI-driven autonomous vehicles could help relieve the need for more drivers in the future.
The practice of data science requires the use of analytics tools, technologies and programming languages to help data professionals extract insights and value from data. A recent survey of nearly 24,000 data professionals by Kaggle suggests that Python, SQL and R are the most popular programming languages. The most popular, by far, was Python (83%). Additionally, 3 out of 4 data professionals recommended that aspiring data scientists learn Python first.
Survey results show that 3 out of 4 data professionals would recommend Python as the programming language aspiring data scientists to learn first. The remaining programming languages are recommended at a significantly lower rate (R recommended by 12% of respondents; SQL by 5% of respondents. Anyhow, Python will also boom more in 2019. But, R community too have come up with a lot of recent advancements. With new packages and improvements, R is expected to come closer to python in terms of usage.
Blockchain and Big Data
In recent years, the blockchain is at the heart of computer technologies. It is a cryptographically secure distributed database technology for storing and transmitting information. The main advantage of the blockchain is that it is decentralized. In fact, no one controls the data entering or their integrity. However, these checks run through various computers on the network. These different machines hold the same information. In fact, faulty data on one computer cannot enter the chain because it will not match the equivalent data held by the other machines. To put it simply, as long as the network exists, the information remains in the same state.
Big Data analytics will be essential for tracking transactions and enabling businesses that use the Blockchain to make better decisions. That’s why new Data Intelligence services are emerging to help financial institutions and governments and other businesses discover who they interact with within the Blockchain and discover hidden patterns.
The broader the canvas of visualization is, the better the understanding is. That’s exactly what happens when one visualizes big data through the Augmented Reality (AR) and Virtual Reality (VR). A combination of AR and VR could open a world of possibilities to better utilize the data at hand. VR and AR can practically improve the way we perceive data and could actually be the solution to make use of the large unused data.
By presenting the data in the form of 3D, the user will be able to decipher the major takeaways from the data better and faster with easier understanding. Many recent types of research show that the VR and AR has a high sensory impact which promotes faster learning and understanding.
This immersive way of representation of the data enables the analysts to handle the big data more efficiently. It makes the analysis and interpretation more of an experience and realisation that the traditional analysis. Instead of the user seeing numbers and figures, the person will be able to see beyond it and into the facts, happenings and reasons which could revolutionize the businesses.
Computing infrastructure is an ever-changing landscape of technology advancements. Current changes affect the way companies deploy smart manufacturing systems to make the most of advancements.
The rise of edge computing capabilities coupled with traditional industrial control system (ICS) architectures provides increasing levels of flexibility. In addition, time-synchronized applications and analytics augment the need for larger Big Data operations in the cloud. This is regardless of cloud premise.
Edge is still in early stage adoption. But, one thing is clear that edge devices are subject to large-scale investments from cloud suppliers to offload bandwidth. Also, there are latency issues due to an explosion of the IoT data in both industrial and commercial applications.
Edge soon will likely increase in adoption where users have questions about the cloud’s specific use case. Cloud-level interfaces and apps will migrate to the edge. Industrial application hosting and analytics will become common at the edge. This will happen using virtual servers and simplified operational technology-friendly hardware and software.
The Rise of Semi-Automated Tools for Data Science
There has been a rise of self-service BI tools such as Tableau, Qlik Sense, Power BI, and Domo. Furthermore, now managers can obtain current business information in graphical form on demand. Although, IT may need to set up a certain amount of setup at the outset. Also, when adding a data source, most of the data cleaning work and analysis can be done by analysts. The analyses can update automatically from the latest data any time they are opened.
Managers can then interact with the analyses graphically to identify issues that need to be addressed. In a BI-generated dashboard or “story” about sales numbers, that might mean drilling down to find underperforming stores, salespeople, and products, or discovering trends in year-over-year same-store comparisons. These discoveries might in turn guide decisions about future stocking levels, product sales and promotions. Also, they may determine the building of additional stores in under-served areas.
Upgrade in Job Roles
In recent times, there have been a lot of advancements in the data science industry. With these advancements, different businesses are in better shape to extract much more value out of their data. With an increase in expectation, there is a shift in the roles of both data scientists and business analysts now. The data scientists should move from statistical focus phase to more of a research phase. But the business analysts are now filling in the gap left by data scientists and are taking their roles up.
We can see it as an upgrade in both the job roles. Business analysts now hold the business angle firm but are also handling the statistical and technical part of the things too. Business analysts are now more into predictive analytics. They are at a stage now where they can use off-the-shelf algorithms for predictions in their business domains. BA’s are not only for reporting and business mindset but now are more into the prescriptive analytics too. They are handling the role of model building, data warehousing and statistical analysing.
How this question is answered will be fascinating to watch. It could be that the data science field has to completely overhaul what it can offer, overcoming seeming off-limit barriers. Alternatively, it could be that businesses discover their expectations can’t be met and have to adjust to this reality in a productive manner rather than get bogged down in frustration.
In conclusion, 2019 promises to be a year where smart systems make further inroads into our personal and professional lives. More importantly, I expect our professional lives to get more sophisticated with a variety of agents and systems helping us get more of out of our time in the office!
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