The world is getting smarter, which implies without data, your enterprise cannot gain a competitive business advantage and succeed at digital transformation. You may have a large amount of unstructured data coming from varied sources and lying everywhere, but if it fails to offer engaging and actionable findings, you are doomed. The ability of an enterprise to compete in the market is driven by how well it leverages data, applies analytics, and most importantly, implements new technologies. Data is a key business asset and you cannot make informed business decisions without practicing the science of data analytics.
In a survey that Gartner conducted among CIOs in the US to learn of their response on the game-changer technology for their business, Data and Analytics (including predictive analytics) topped with a 24% score. Businesses believe that the data analysis process of data analysis process leads them to act on data and gain valuable insights to achieve digital transformation.
The first step in data analytics, however, begins with data preparation.
Before you analyze data and come to the right conclusions, you need to ensure proper data collection, clean it, and convert all the data in a structured format, guaranteeing high data quality.
You have access to:
- Data Lake
- Data Warehouses
- Social, Geolocation, and IoT data
- Third-party data on consumers and employees, and
- Complex data
This shows how data is omnipresent.
On top of that, everyone needs numbers in real-time, whether they are data analysts, scientists, or consumers. Real-time and just-in-time data make you more production-ready. As you prepare data, ensure data access is synthesized across the organization and embedded within applications.
What is Data Preparation?
In the words of Gartner, “Data preparation is an iterative-agile process for exploring, combining, cleaning, and transforming raw data into curated datasets for self-service data integration, data science, data discovery, and BI/analytics”.
Analysts, data scientists, and citizen data scientists employ data preparation tools to carry out the data preparation process. These tools offer data access for use in storage, physical and logical data modeling, and data manipulation to implement data integration, data visualization, and data analytics. Besides, there are a few data preparation tools that support ML algorithms with the ultimate objective of augmenting and accelerating data preparation.
Remember data should never overwhelm you, as it is coming from anywhere, everywhere. Data should never act as an obstacle. There are so many dashboards to look through and there is too much for businesses to consume. As businesses are overloaded with data, they fail to act on relevant information. So, the question is, how enterprises can make faster, prudent decisions with a limited view of the data.
Strategic analysis is, of course, impractical. When data analysts examine data to prove their hypothesis right, they are left with a tunnel vision and actionable decision-making fails.
Augmented Analytics is the only way out here.
What do you mean by Augmented Analytics?
As mentioned in Gartner’s October 2018 research publication, Augmented Analytics Is the Future of Data and Analytics, “Augmented Analytics uses ML and AI techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment”.
Businesses are increasingly using augmented analytics capabilities as a primary feature of broader data management, data preparation, modern analytics and business intelligence (BI), and data science and machine learning (ML) platforms.
- Automates aspects of finding and surfacing the most important business insights to optimize decision making. Augmented analytics can do so in a fraction of the time, using minimal data science and ML skills.
- Employs ML/AI techniques to automate model selection (autoML), feature engineering, model operationalization, model explanation, as well as model tuning and management.
- Creates human-generated and autogenerated ML models to embed them in enterprise applications.
- Automates data insights through Natural Language Processing (NLP) and ML to allow more interactions across the organization and make better predictions for effective data storytelling.
Effects of Augmented Analytics on Your Organization & Skills
- Democratizes insights from analytics to all kinds of business processes and roles, thereby, reducing dependency on expert analytics, ML, and data science skills. Most importantly, using ML enables unbiased data preparation.
- Opens ML and data science content creation to citizen data scientists, making them more productive and collaborative, and freeing them for high-value tasks.
According to a 2019 poll survey conducted by Gartner, around 50% of businesses are exploring the possibilities to use augmented analytics. While 30% of them are expected to explore this science in the next 6-12 months.
A few of the use cases of Augmented Analytics
Patient wait time forms an indomitable part of patient care quality. Augmented analytics enables employees across hospitals to engage with data on patient wait time and understand how successfully they reduce it. Augmented analytics platforms offer instant answers to searched queries, besides answering additional questions hospital staff failed to ask.
The AI-powered engine lends transparent and valuable insights to reveal hidden inefficiencies and key areas that are worth addressing. Remember this form of data analytics augments and enhances human thinking, taking maximum intellectual pressure out of routine analytics tasks.
Learn why small businesses should embrace data analytics.
Augmented analytics also finds its application in the detection of unusual images from surveillance. The Government and the public sector benefits from the analysis of the information captured through surveillance cameras. These benefits show in the form of event prediction, goal-driven analysis, and online monitoring applications, as well as anomaly and intrusion detection.
There has been a growth in the use of various AI techniques, like convolutional neural networks, to detect anomalies. These networks employ deep learning techniques to significantly improve the accuracy of anomaly detection.
Talking about surveillance, Government agencies can also leverage investment in visual data through computer vision applications. A sub-specialty within the wider field of AI technology, computer vision apps are crucial to translating growing image and video stores into actionable intelligence as well as insights. It is the cloud APIs that make computer vision usually accessible to different app types.
Financial fraud is growing at a fast pace and this problem costs businesses trillions of dollars, despite using much complex fraud detection tools. This is where graph data science comes to your rescue.
A graph data science approach enhances the feasibility and accuracy of the prevalent fraud detection methods by augmenting ML pipelines and existing data analytics. This leads to less fraudulent transactions and highly secure revenue streams. Fraud investigation and data science teams can leverage graph technology to identify first-party fraud and complex fraud rigs.
Businesses have access to loads of data, but it makes no sense, if they fail to make informed decisions using it. It is the data analytics process that leads businesses to effectively act on data and gain valuable insights to ensure digital transformation success. And then augmented analytics is the present and the future of data analytics. Using AI and ML techniques, augmented analytics finds applications in surveillance, fraud detection, hospital operations, market intelligence, and much more.
Are you exploring the possibilities of employing augmented analytics in your organization, or do you plan to use it in the upcoming 6 months to 1 year?