Skip links

Optimizing Data for Peak AI Performance: Strategies and Techniques

Jump To Section

This article was published on Information Management.
ML and AI promise to be transformative technologies

Data Mastery for Peak AI Performance

Harnessing the Power of Machine Learning and artificial intelligence holds the potential for transformative technologies. However, as businesses embrace machine learning, establishing a solid foundation remains a challenge. The key lies in controlling the quality and accuracy of data, ensuring that the power of machine learning can be fully realized.

In fact, a recent report found that nearly half of businesses do not have the technology in place to leverage their data effectively. That same report noted that obtaining accurate data was one of the largest challenges businesses face when it comes to data management.

New open source technologies now enable companies of any size to implement advanced analytics, but most companies fail at the basics of collecting and storing their data. It is the old “garbage-in, garbage-out” problem, but now poor data is driving machine learning or artificial intelligence projects.

Relevancy and timeliness of data is critical to effective application of machine learning for business outcomes, both in training and using the model. That said, the timeliness needed depends on the use case. It could be in seconds, minutes, hours or days.

Also read: AI Augments and Human Intelligence: An Evolution

Not all data needs to be refreshed in real time. Historically, data collection and curation have been batch-oriented. The increasing corporate appetite for real time analytics is changing that, and the abundance of elastic computing and storage is making the change possible.

What is AI Optimization?

AI optimization involves leveraging artificial intelligence algorithms to enhance and streamline processes, systems, and decision-making, ultimately maximizing efficiency, performance, and outcomes across various domains.

Real-Time Data Mastery: Tools and Techniques for Effective Digital Transformation

Once the sole province of companies such as Amazon, Citibank or PayPal, various proprietary and open source technologies are now available to help organizations of any size tackle these challenges. Data pipelines, asynchronous messaging, micro batches, stream processing, time series and concurrent model iterations are representative techniques that are being deployed successfully.

Apache Streamsets, Kafka, Spark, Time series databases, and Tensorflow are some of the foundational open source tools and technologies in the forefront of this shift to real time data collection and curation.

But no matter how sophisticated the technology, it still comes down to the relevance and timeliness of the data. It is the foundation of any digital transformation effort, and companies must take a disciplined and structured approach to managing their data if they want to properly leverage machine learning and AI. This involves:

An understanding of the business case. What are the business goals and objectives? What data are relevant to achieving understanding if those goals are to be met? What level of timeliness is needed? Without understanding the answers to these questions, any effort to leverage data will likely fail to reach its full potential.

A full inventory of data sources. This includes structured data from internal transactional databases; external sources, such as credit scores from TransUnion or Experian, to augment the internal data; and then open source and internal, unstructured data on user behavior and social media. Many companies think their internal structured data is enough, but the unstructured and third-party data can be just as critical.

A strategy for storing the data properly. For many companies, important data is distributed in silos across the enterprise. For example, the customer onboarding system is disconnected from the website shopping cart, while the sales team is working with the CRM system to manage cross-selling. Implementing a data lake will help pool these different data sources into a single view across the enterprise. In addition, groups across the enterprise will make decisions based on the same source of data, eliminating redundant and inconsistent actions.

Leveraging the data for visualization. Once the basics of data collection are established, then companies can move towards using the data for visualization, where reports and dashboards enable people to make decisions and take actions based on the data. This is the first step in providing meaningful interpretation of data in a form that is actionable.

A move to automated decision making and machine learning. With clean, timely, and relevant data – and a solid understanding of how the data can be used to make decisions – it now becomes possible to forecast and predict in real time. Rather than having to conduct interpretation of the data manually, companies can let machines use the data to automate some of the decision making. Additionally, unsupervised machine learning also enables to uncover insights which previously have not been hypothesized.

A commitment to on-going data governance. It’s important to establish policies and processes that maintain a high level of data consistency and cleanliness, otherwise companies will find the quality of their analytics will degrade as the quality of their data degrades. When that happens, it opens the door to a sub-optimal decision making process and has an adverse impact on clients

There is no silver bullet for this, nor should companies expect to implement a comprehensive data strategy in one fell swoop. Rather, this is a long, slow process, assessing where the company is today in its maturity curve and what it needs to do to get to the next step.

If there are 50 to 100 data sources that ultimately need to be integrated, don’t try to incorporate all of them at the same time. Instead, focus on the two or three that will have the greatest impact on the business outcomes and work those through the full end-to-end process of data assessment, enrichment, visualization, and ultimately machine learning.

How can AI be used to optimize?

AI can optimize processes by analyzing vast datasets, identifying patterns, and making real-time adjustments to improve efficiency, accuracy, and decision-making across various domains such as manufacturing, healthcare, finance, and transportation.

Picture of Anil Somani

Anil Somani

Suggested Reading

Ready to Unlock Your Enterprise's Full Potential?

Michael Woodall

Chief Growth Officer of Financial Services

Michael Woodall, as the Chief Growth Officer of Financial Services at Altimetrik, spearheads the identification of new growth avenues and revenue streams within the financial services sector. With a robust background and extensive expertise, Michael brings invaluable insights to his role.

Previously, Michael served as the Chief of Operations and President of the Trust Company at Putnam Investments, where he orchestrated strategic developments and continuous operational enhancements. Leveraging strategic partnerships and data analytics, he revolutionized capabilities across investments, retail and institutional distribution, and client services. Under his leadership, Putnam received numerous accolades, including the DALBAR Mutual Fund Service Award for over 30 consecutive years.

Michael’s dedication to industry evolution is evident through his involvement with prestigious organizations such as the DTCC Senior Wealth Advisory Board, ICI Operations Committee, and NICSA, where he served as Chairman and now holds the position of Director Emeritus. Widely recognized as an industry luminary, Michael frequently shares his expertise with various divisions of the SEC, solidifying his reputation as a seasoned presenter.

At Altimetrik, Michael plays a pivotal role in driving expansion within financial services, leveraging his expertise and Altimetrik’s Digital Business Methodology to ensure clients navigate their digital journey seamlessly, achieving tangible outcomes and exponential growth.

Beyond his corporate roles, Michael serves as Chair of the Boston Water & Sewer Commission, appointed by the Mayor of Boston, and is actively involved in various philanthropic endeavors, including serving on the board of the nonprofit Inspire Arts & Music.

Michael holds a distinguished business degree from Northeastern University, graduating with distinction as a member of the Sigma Epsilon Rho Honor Society.

Anguraj Kumar Arumugam

Chief Digital Business Officer for the U.S. West region

Anguraj is an accomplished business executive with an extensive leadership experience in the services industry and strong background across digital transformation, engineering services, data and analytics, cloud and consulting.

Prior to joining Altimetrik, Anguraj has served in various positions and roles at Globant, GlobalLogic, Wipro and TechMahindra. Over his 25 years career, he has led many strategic and large-scale digital engineering and transformation programs for some of world’s best-known brands. His clients represent a range of industry sectors including Automotive, Technology and Software Platforms. Anguraj has built and guided all-star teams throughout his tenure, bringing together the best of the techno-functional capabilities to address critical client challenges and deliver value.

Anguraj holds a bachelor’s degree in mechanical engineering from Anna University and a master’s degree in software systems from Birla Institute of Technology, Pilani.

In his spare time, he enjoys long walks, hiking, gardening, and listening to music.

Vikas Krishan

Chief Digital Business Officer and Head of the EMEA region

Vikas (Vik) Krishan serves as the Chief Digital Business Officer and Head of the EMEA region for Altimetrik. He is responsible for leading and growing the company’s presence across new and existing client relationships within the region.

Vik is a seasoned executive and brings over 25 years of global experience in Financial Services, Digital, Management Consulting, Pre- and Post-deal services and large/ strategic transformational programmes, gained in a variety of senior global leadership roles at firms such as Globant, HCL, Wipro, Logica and EDS and started his career within Investment Banking. He has developed significant cross industry experience across a wide variety of verticals, with a particular focus on working with and advising the C-Suite of Financial Institutions, Private Equity firms and FinTech’s on strategy and growth, operational excellence, performance improvement and digital adoption.

He has served as the engagement lead on multiple global transactions to enable the orchestration of business, technology, and operational change to drive growth and client retention.

Vik, who is based in London, serves as a trustee for the Burma Star Memorial Fund, is a keen photographer and an avid sportsman.

Megan Farrell Herrmanns

Chief Digital Officer, US Central

Megan is a senior business executive with a passion for empowering customers to reach their highest potential. She has depth and breadth of experience working across large enterprise and commercial customers, and across technical and industry domains. With a track record of driving measurable results, she develops trusted relationships with client executives to drive organizational growth, unlock business value, and internalize the use of digital business as a differentiator.

At Altimetrik, Megan is responsible for expanding client relationships and developing new business opportunities in the US Central region. Her focus is on digital business and utilizing her experience to create high growth opportunities for clients. Moreover, she leads the company’s efforts in cultivating and enhancing our partnership with Salesforce, strategically positioning our business to capitalize on new business opportunities.

Prior to Altimetrik, Megan spent 10 years leading Customer Success at Salesforce, helping customers maximize the value of their investments across their technology stack. Prior to Salesforce, Megan spent over 15 years with Accenture, leading large transformational projects for enterprise customers.

Megan earned a Bachelor of Science in Mechanical Engineering from Marquette University. Beyond work, Megan enjoys playing sand volleyball, traveling, watching her kids soccer games, and is actively involved in a philanthropy (Advisory Council for Cradles to Crayons).

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes