What enables clients to transform their businesses? What keeps them awake at night to rewrite customer stories? It is the reliable data that gives analysis and insights for enterprise owners to decode, innovate and gain customer’s trust. For efficient data analysis, digital technologies play an inevitable role in business and Artificial Intelligence and its dominant form, Machine Learning has become the most sought-after technology now to help innovate and transform businesses.
Increasingly, organizations are realizing the importance of analytics in their businesses and digging deeper into data to increase its effectiveness to gain competitive advantage. To achieve reimagined business objectives, organizations are considering implementing machine learning and artificial intelligence along with analytics in their daily tasks.
Imagine a scenario where you are diagnosed with high blood pressure just by your retina scanning. This imagination can turn into reality soon leveraging and correlating multiple data sets and applying advanced AI techniques. Through deep learning techniques, data can be automatically recognized to classify images, text, and speech with greater precision. This has led to an impressive development of applications in text and speech recognition, imaging analytics, Natural Language Processing (NLP), and many more such breakthroughs in different industries etc.
To move ahead of competition and to get an edge over others in the industry, organizations are engaging in machine-learning based predictive analytics. Neural networks and deep learning algorithms successfully discover and utilize hidden patterns in unstructured data sets and reveal new information from these data sets. Today, where businesses are able to collect data about their customers within seconds, it is very important for them to quickly process and extracts real-time insights from the data. Organizations will have to build a strategy to harness large volumes of big data in near real-time and re-wire several of their business processes.
Powered by cloud, containerization, APIs and microservices architectures, the modern IT landscape looks very different from what it was even 5 years ago. Many organizations have invested in Descriptive Analytics in the past and have reaped benefits, but it is now time to take advantage of advances in Analytics.
Let’s take a quick look at the three types of analytics, to begin with:
- Descriptive analytics – It is the basic form of analytics which aggregates big data and provide useful insights from past records
- Predictive analytics – A popular concept now, it uses historical data, artificial intelligence and machine learning to predict the future outcomes
- Prescriptive analytics – Analytics which uses a combination of business rules, machine learning, and computational modelling to recommend the best course of action for any pre-specified outcome typically implemented through workflows
Companies apply all the three types of analytics while working on a data set, but the use of predictive analytics drives more value for businesses as it helps them to anticipate future outcomes. Organizations will have to set an equilibrium within their data, technology, and employees, to completely transform their businesses to an AI-driven predictive analytics model which is smarter and helps in faster decision making. Implementing AI requires moving to a data driven culture coupled with leveraging advanced technologies at an enterprise level. This requires capital investment, infrastructure changes and workforce training for seamless integration of technologies in the business.
In 2019, the increased investments in digitization will drive businesses to make AI go mainstream with more focus on harnessing the power of deep learning techniques and accessing synthetic data to simulate for meaningful insights.
The capabilities that drive AI and predictive analytics can be applied to almost any business domain in any industry one can think of, like securing the IT work environments, detecting cyber security and data security frauds, and thefts etc.
Many organizations are focusing on Proof of Concepts (PoCs) and experimenting with Artificial Intelligence, some of which have made it to production. Typically, these have been localized initiatives leveraging a limited set of data with well-defined outcomes. Given the multitude of options available in the market in terms of tools and programming options, the PoCs are typically done differently. But, for organizations to achieve scale and see real-value, it is important to build on a solid foundation, much like how traditional software is being developed. However, there will be differences compared to traditional software SDLC since much of the model development will be iterative based on hypotheses. Nonetheless, it would make sense to have a standard approach including tools and technologies.
To harness the available data, it becomes critical to have Data Governance processes in place including Data Quality. Big Data requires cataloguing of what’s available and its associated quality metrics before it can be used for any type of Analytics. Given that a lot of Machine Learning is happening on cloud-based scalable infrastructure, it becomes important for organizations to be able to push the data on to Cloud storage where compute power is unconstrained.
Combining Big data with predictive analytics leveraging cloud computing can do wonders and provide important insights immediately and accurately. Without exception, this is going to be the key approach that can be adopted across all industries.
The possibilities for AI are endless. It will enable machines to learn, reason, solve problems, and understand language instantaneously. But those capabilities may be years away. Big Data Analytics and Machine Learning – are here today, allowing marketers to use intelligence to detect, learn, and optimize operations.