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5 Trends Which Suggest Machines Are Taking Over Analytics

5 Trends Which Suggest Machines Are Taking Over Analytics
April 19, 2017

Until recently, most data originated from humans so it made sense when humans were involved in the processing of data. The era of Big Data marked the foundation of critical roles like data scientist. But with the emergence of the internet of things and automation and the rapidly developing capabilities of Big Data and Analytics, machines and algorithms are taking over roles which only humans could do in the past.

That said, we are still far from fully automated analytics. But with the pace of change in the analytics and automation, we can look forward to rapidly increasing automation in the whole data management ecosystem.

Here are some interesting trends that anyone interested in Big Data and Analytics cannot ignore:

  1. Automation as a pivot – the era of ‘virtual data scientists’

    A Forrester survey reports investments in artificial intelligence (AI) will increase by 300% in 2017, propelling significant outcomes in the field of analytics. Machine learning will be able to analyse data at a scale human possibly could not, driving faster business decisions and bridging the gap from insights to action.

    As AI begins to address much of the traditional reporting and query requirements, ‘data scientists’ or ‘data science’ as a specific field of work will witness a transition. Software like ‘Automatic Statistician’ will generate dense reports with texts and charts, describing the mathematical trends it locates, turning metrics and figures into smart interpretative ideas.

    Beyond operational simplicity and complex task management, automation will also help bolster speed, scale, and accuracy - a core requirement for sustainable analytics.

  2. Natural Language Generation (NLG) spearheads Business Excellence

    By 2019, Natural Language Generation is expected to become a standard feature of modern BI and analytics platforms. NLG is ushering in a new era of BI - carrying enormous potential to improve user experiences of next-generation smart data discovery platforms. Data analysis, traditionally delivered in tables, charts and graphs, will now become a continuous, focused narrative, allowing business users to glean immediate, actionable knowledge. 

  3. Analytics marking the end of outmoded legacy processes in the Government systems

    Government agencies tend to produce massive amounts of data on daily basis. It is not only difficult to manage and organise these data sets manually but also to extract insights out of it. However, with self-service analytics and BI tools, tackling daily data gatherings tasks and reporting can be made efficient and effective. This will also help alleviate common problems in these agencies with respect to staff shortage and fraudulent activities.

    The legacy methods of gathering official statistics have come under consistent scrutiny for cost and efficiency challenges. This has prompted governments to adopt Big Data technologies to improve their data gathering approach. Replacing the traditional decennial population census, Big Data provides equivalent estimates while eliminating the need to physically canvass streets prior to the start of the population count.

    Governments are now reengineering a majority of their census processes, including data collection techniques, methodologies and field structure in order to increase efficiencies and reduce costs. The US census bureau estimates a savings of approximately $5.1 billion in 2020, as compared to their 2010 Census.

    Web scale technologies like Hadoop offer scalability and NoSQL database like Cassandra make dissemination of results faster and more efficient.

  4. Emergence Analytics of Things with convergence of IoT, cloud and Big Data

    IoT market is set to observe growth to become $15 billion market by 2020. The original IoT concept revolved around the network of connected objects without specific focus on data and cloud computing. But the discussions have now evolved to realize that there is inherent need to see what can be done with the vast amount of data that these billions of connected objects are generating.

    The organisations are now more focussed on devising ways to generate insights out of the data collected from the connected devices, sensors and machines with minimal manual effort. The result is the growing demand for analytical tools that can seamlessly connect with cloud hosted data sources and help organisations get more benefits out of their IoT investments.

  5. Humanizing the platform – the emerging query framework

    By 2019, 50% of analytics queries will find greater layering, denser levers for stratification and perfectly streamlined user/customer preference and trend mapping.

    Search, natural language query or voice, or auto-generated mapping options will be the order of the day, taking request personalization to a whole new level of engagement and connectivity.

    Search engines like Google and Bing are already in the process of homogenizing data searches with everyday conversation via a ‘natural language search’, as opposed to default keyword-based alternatives. Companies are launching software with individual style guides and sample phrases to be leveraged in order to generate simple queries in voice.

We are only at the tip of the iceberg. As these trends indicate, the market is all set to explode in the near future. From a business initiative to a business imperative, the landscape will continue to evolve at breakneck speed, with new channels and vibrant modes of delivery pushing the boundaries.

The future is clearly bright for analytics, as it is set to become a near-sentient, all-encompassing tool affecting businesses, human lives, and way of working at every conceivable touch point.