Modern data analytics yields valuable advantages across all industries. It can identify market opportunities, determine how to boost sales, and help deliver more relevant products. It can help businesses understand customers at the granular level, allowing them to meet their needs. Besides, it helps organizations track their performance and enhance strategy and decision-making at the highest level. It can even identify data anomalies to mitigate risk and target fraud. And this is just the beginning.
Businesses have been collecting and analyzing data for decades. Traditional data projects are complex and require months of work by highly specialized teams, but today’s markets change so quickly that organizations can no longer afford to wait. By the time reports are completed, the information may already be outdated.
The amount of data requiring analysis is also growing at a faster rate than traditional data scientists can keep up with. According to a recent IDC report, worldwide data will increase by 61% to 175 zettabytes by 2025. This is critical information that modern businesses cannot afford to ignore.
Legacy analytic models are also too narrow in scope. They generally address one phase of a given process for one type of user. This creates ‘operational silos' that were cut off from other business processes, people, and technologies, leading to insulated decision-making, to the detriment of the organization.
What modern data analytics platforms seek to do is replace the time-consuming, insulating operations of legacy processes with a platform that leverages centralized data and analytics. It breaks down silos, increases collaboration, and enhances efficiency. This is the rationale behind self-service data analytics.
The Value of Democratized Data Analytics
With an end-to-end self-service data science and analytics platform, organizations can democratize advanced data analysis. This enables users to generate accurate and meaningful business intelligence with minimal support from data scientists. When information is easier for users to access and understand, there is more room for creativity and collaboration, allowing organizations to come together and make data-driven decisions.
Business users with various specializations and responsibilities can also utilize data to solve problems in unique ways based on their own knowledge and objectives within the organization. This empowers them to become more significant assets to the company.
Equipping business users also improves efficiency. Early theories can be tested quickly and cost-effectively to facilitate dynamic decision-making and problem-solving. In a continually evolving market, this gives businesses more lead-time to identify trends and capitalize on opportunities to allow for better accuracy in business forecasting. This translates to greater organizational agility and a shorter time to market.
Self-service analytics and data democratization are not new concepts. They have been empowering users to create insights and drive change for years. However, descriptive and diagnostic analytics—finding out what happened and why—is just the beginning. How do organizations address high-impact, future-facing questions, such as predicting future trends and how to implement better business strategies to strengthen the bottom line?
Role of AI and Deep Learning in Data Science
This is where artificial intelligence (AI) and machine learning (ML) come in. In contrast to traditional analytical models that relied on highly specialized and comparatively rare skillsets, modern data analytics employ AI and ML to enhance analysis. Not only does this allow for advancements in data science, but it also helps accelerate processes, improve efficiency, and drive economies of scale.
AI data design requires advanced input to build its workflows. This must be supported by cutting-edge technologies such as deep learning, automated machine learning, and augmented analytics to automate data preparation and discovery at a level beyond human capability. At the same time, the system must simplify and communicate actions to business users.
This is the future of data science. Organizations across the world are investing tens of billions of dollars in AI and ML in a global race to gain a competitive edge. In fact, Gartner’s Enter the Age of Analytics report predicts that by 2023, AI and deep-learning techniques will be the two critical focuses for new data science applications.
For those working with data, advancements in AI and ML will undoubtedly impact how insights are derived from data. However, while technology and automation are crucial for enhancing our ability to make decisions and respond faster, human intuition and experience are still irreplaceable. Humans will always be at the center of reasoning and decision-making, while technology and automation simply enhance these abilities.
Investing in data democratization requires C-level commitment. Time and capital will be needed to phase out legacy systems and introduce new processes in anticipation of industry developments. More than that, there will need to be a cultural shift, requiring buy-in from all lines of the business.
As businesses progress in their analytic journeys, executive managers will need to drive the adoption of self-service data science and analytics from the top-down. It helps to create a culture of ‘citizen data scientists' across the organization as employees learn to become data users. This will enable employees to harness more sophisticated business intelligence and better adapt to machine learning.
Breaking away from legacy systems and entrenched thinking will be critical in creating this new culture. Employees who demonstrate innovative thinking and embrace new technologies should be championed and encouraged to advance these technologies across their teams.
Organizations that can modernize their analytic journey and unify their data landscape will have a better chance of succeeding in achieving a real digital transformation.
Celine Siow, regional vice-president, Asia and Japan, Alteryx wrote this article.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.