Data Analytics Technologies TrendsDec 01, 2019
After completing this video, you will be able to recognize the key trends in analytics technology. When it comes to effective data analytics, data storage, and database technology, analytics software technology, and data visualization technology are all critical tools. And while it's useful to examine these analytics technology categories independently, it's important to also keep an eye on four technology trends that are impacting these technologies at a fundamental level.
When it comes to effective data analytics, data storage, and database technology, analytics software technology, and data visualization technology are all critical tools. And while it's useful to examine these analytics technology categories independently, it's important to also keep an eye on four technology trends that are impacting these technologies at a fundamental level. First, there's the consolidation and integration of analytics technologies into platforms. Second, the emergence of open source options. Third, cloud computing. And fourth, artificial intelligence and machine learning. It bears taking a closer look at each of these individually.
First is the consolidation and integration of the three technologies into unified analytics platforms. Most technology brands initially focused on an individual area of the technology stack. But in an effort to stay relevant, they've all expanded into the other areas or created the ability to link or integrate with other providers in the stack. The growing use of data lakes and data marts fits neatly with the capabilities of unified analytics and business intelligence platforms that can take in new types of raw and unstructured data.
Take, for example, the case of Tableau, which began as data visualization on top of analytics tools, but now claims to cover the entire stack. For its part, SAS started out as statistical software, until it added visualization in response to the emergence of Tableau. And database provider Teradata repositioned itself as more than an advanced data warehouse through integrations with other tools. The second trend is the emergence of open source options within each of the technology categories. Open source software is developed and shared more freely, often at no charge, than software controlled by established vendors. But at the same time, those established vendors are also collaborating in open source initiatives.
Consider some examples of open source. Within data warehousing, the best example of open source is Hadoop which is widely regarded as a viable alternative to the traditional commercial database solutions. Within the analytics software area, the best example is R, a good open source alternative to traditional commercial statistical programming languages. And within the data visualization area, there are numerous non-commercial options including Google Charts and Google Data Studio, both available for free. The third major analytics technology trend is cloud computing. The availability of cloud computing is forcing enterprises to make a call on where their analytics work will be conducted - on-premises, or in cloud computing environments.
Microsoft, Amazon, and Google all now offer cloud analytics environments that integrate the three analytics technology categories of data storage, analytics, and visualization in an unlimited environment. The cloud computing phenomenon has also blurred many traditional lines of separation in the data and analytics process. Managed cloud storage solutions like Amazon Redshift, Google BigQuery, and Snowflake offer much larger storage capacity and better data processing power than traditional warehouses that sit inside enterprise IT environments. Cloud computing also facilitates the Internet of Things. That enables the ability to perform analytics at the point where the business process is taking place. For example, in an automated wind farm, or on a delivery truck in transit.
The final trend is artificial intelligence or AI and machine learning. More and more, these technologies are being used behind the scenes in business applications, and indeed, in everyday life. You can see it in using chatbots for conversing with customers, and the emergence of self-driving cars, for example. To define it simply, artificial intelligence refers to a variety of information and other technologies that try to simulate human intelligence. Reading text, recognizing images, recognizing and generating speech, and drawing conclusions in complex situations. Machine learning refers to a more specific set of technologies that recognize patterns in data in unsupervised fashion.
In other words, it's about recognizing patterns without being told what to look for. These technologies can simulate the ability of humans to learn from experience, and improve over time. When they have trained on a sufficient amount of data, machine learning models are incorporated into specific artificial intelligence applications such as image recognition or advanced analytics. When thinking about the whole area of analytics technologies and tools, it's best to bear in mind that change is the constant. Tools that are innovative or cutting edge today may not be in the future.
As of now, however, data and analytics technologies are being integrated into comprehensive and powerful platforms. Cloud computing is changing where analytics are processed, and offering large scale resources to businesses of all sizes. Many open source tools are now on equal footing with commercial tools, enabling advanced analytics at low technology cost. And businesses are just scratching the surface of what can be done with analytics, in combination with the technologies of artificial intelligence. It's an exciting time, and tomorrow will likely bring more.