One of the biggest challenges for Chief Data Officers (CDOs) and Chief Analytics Officers (CAOs) is finding innovative ways to access, govern and integrate their organization’s data to make informed decisions. on the data. Certain aspects of artificial intelligence (AI), such as machine learning (ML), natural language processing (NLP), and deep learning, have become valuable disciplines for CDOs and CAOs to explore due to of their transformative potential. AI and ML are rapidly changing the way organizations do everything from optimizing product performance and improving customer experiences to predicting consumer behavior and much more.
Systems that learn—and evolve
As the people responsible for their company’s data analytics operations, CDOs and CAOs must foster the environment necessary for continuous improvement. This means identifying patterns and correlations from millions of data points and being able to make data-driven decisions for their entire organization. Machine learning platforms can help organizational leaders sift through massive data sets and uncover previously hidden efficiencies, an invaluable advantage in a business environment where everyone is looking for an advantage. You can design a machine learning algorithm to start with a desired outcome, like reducing costs in a certain process, and the platform will look for interacting variables that, if changed, are most likely to achieve that objective. Adding machine learning algorithms is a powerful way to increase the benefits of analytics-informed decisions.
Machine learning often works best when applied to large volumes of data and/or a wide variety of data, where scale and complexity may present barriers to revealing insights without the capabilities of additional learning of the models. Larger data sets provide greater opportunities to identify patterns that would otherwise be difficult to detect. AI and ML are changing the way organizations think about analytics, and forward-thinking CDOs and CAOs are well-positioned to make the case for integrating next-generation platforms into the company’s analytics toolbox. their organization.
Go beyond conventional data
Data analytics platforms become even more valuable and in-depth when they can leverage unstructured data, such as speech and text. NLP capabilities can enable machines to understand nuanced aspects of human language, such as inflection, slang, sarcasm, and more. NLP uses grammar analysis, part-of-speech tagging, lemmatization (reducing words into a single form), morphological segmentation (dividing words into units), and sentence hyphenation to analyze speech data. These tools provide a richer and more precise foundation for data analysis in areas such as prediction, text-to-speech, cognitive search, natural language interaction, and natural language generation.
This is particularly important in areas such as social media analytics, emails and surveys, and contact center conversations, where understanding the nuances of language is crucial. Integrating NLP can help transform once-qualitative data into actionable insights, provide competitive advantage, and improve relationships with customers and stakeholders at all levels.
Driving business-wide results
The immediate benefits of integrating machine learning into an organization come from identifying and solving the most feasible problems that artificial intelligence was designed to solve. When deployed correctly, ML algorithms can quickly use data to do everything from reducing line managers’ workload to monitoring social media interactions. CDOs and CAOs can initially deploy machine learning as a limited application in areas that directly impact revenue and costs to provide proof of concept to support broader deployment.
In the long term, an organization will build better models through a better understanding of key organizational areas, identification of gaps and blind spots, and a deeper understanding of customer behavior. These advancements can drive radical organizational transformation and lead a company to become a data-driven organization.
Initially, the integration process may involve trial and error as relationships between data sets are identified, but good data governance can go a long way to reducing errors during model creation. The quality of models depends on the quality of the data on which they are based. It is therefore essential to ensure that data is prepared and quality controlled to ensure that an organization gets the most out of its ML-based platform.
Better decisions, faster
Investing in AI-powered applications allows leaders to make their organizations more innovative and productive. For example, with SAS, CDOs and CAOs have access to an intuitive user interface that helps teams create machine learning models and implement iterative machine learning processes smoothly. With SAS technology, you can easily deploy machine learning algorithms such as decision trees and k-means clustering to extract maximum value from your data.
CDOs and CAOs must accelerate the adoption of AI in data analytics to capitalize on the opportunity to gain key competitive advantages through the collection, identification and use of data from high quality. Integrating machine learning and artificial intelligence into an organization’s analytics ecosystem can supercharge a company’s ability to improve its product strategy, generate new revenue streams, and improve efficiency overall of his business.
NEXT ACTION STEPS
- Help your team stay up to date with automated machine learning (AutoML) and other areas of AI to increase the impact of their work.
- Target inefficiencies in your data reporting that might be ripe for automation. This can free up your data scientists for more rewarding work, like interpreting data.
- Lead efforts to ensure forecasting techniques and other forms of predictive analytics are used to help the business prepare more effectively for the future.
- Supplement your internal data with third-party data sources to gain new sources of information and better decision-making,
- Be sure to apply NLP techniques to social media data to gain clearer, more objective views into how your customers think and interact.
- Ensure your traditional silos don’t hinder AI efforts by supporting the training your staff needs to learn new skills.
- To ensure success, before starting an AI project, clearly identify the business problem you want to solve. What questions do you want to resolve?
