6 ways to attain top benefits from artificial intelligence & machine learning

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Data is the new strategic asset, the biggest business asset today. Data is to today’s digital economy what electricity was to the industrial economy.

Organizations that understand the value of their data have been excited about the prospects of leveraging artificial intelligence (AI) and machine learning (ML) for smarter insights. They have invested in AI and ML tools and technologies, but have yet to see quantifiable benefits from their investments.

Others are reluctant to even start, with a combination of skepticism, lack of expertise, and lack of confidence in the reliability of their datasets holding them back.

But they may have to take the leap sooner as the role of AI and ML gets bigger in business insights. Forrester Research predicts that artificial intelligence will drive the Insights revolution and “truly insights-driven businesses will steal $1.2 trillion per annum from their less-informed peers by 2020.”

AI and ML can be used in multiple ways to harness the power of data, and each brings its own qualities and applications. It can be overwhelming to choose, with huge possibilities of implementation approaches. Here are six effective ways to attain quantifiable benefits from AI and ML.

1) Focus on Information Augmentation (IA) to Organize Data

Big data leads to bigger assets. For many organizations though, the reliability of these assets is still a challenge. Business teams have lost patience with the speed and efficiency in which they are able to get reliable, relevant, and actionable data; and have invested in their own self-service data preparation, visualization and analytics tools, while others have even employed specialized data scientists.

The common refrain is that data first has to be made reliable, and connected with the rest of the enterprise, so that it can be trusted for use in critical business initiatives. Isolated initiatives such as MDM and Hadoop-powered data lakes have not been very successful, and have raised doubts about their value.

Information augmentation is the logical first step to getting data organized in a manner so that it can be reconciled, refined and related, to uncover relevant insights that support efficient business execution across all departments, while addressing the burden of regulatory compliance.

Organizing data across any data type or source, with ongoing contribution and collaboration on limitless attributes, describes a state of continuous IA that organizations should achieve before they can consider AI or ML as a potential next step.

2) Utilize an Insights Platform that Enables AI Applications

Data capital requires new computing infrastructure and a deep understanding of creating applications that analyze data and use the information effectively. Organizing data for the benefit of AI, ML or other initiatives results in clean, reliable data that is connected and forms a trusted foundation.

While being data-driven continues to be in vogue, companies have achieved surprisingly little in the way of measurable, quantifiable outcomes for their investments in technologies and tools. Some of the total cost of ownership (TCO) metrics such as savings realized from switching to cloud vs. on-premises are evident, but there has not been a clear direct correlation between data management, BI, or analytics and the current wave of AI and ML investments.

What’s missing is a proven system of capturing a historical baseline and comparing it to improvements in data quality, generated insights, and resulting outcomes stemming from the actions taken.

Much of this gap can be attributed to the continued disconnect between analytical environments such as data warehouses, data lakes and the like, where insights are generated; and operational applications where business execution actually takes place.

Self-learning data management technology can power data-driven applications that are both analytical and operational, delivering contextual, goal-based insights and actions, which are specific and measurable, allowing outcomes to be correlated, leading to that return on investment (ROI) -- the Holy Grail, and forming a foundation for ML to drive continuous improvement.

Acting as technology portfolio managers for large and small companies that want to focus on nimble and agile business execution, self-learning data platforms are also multi-cloud, keeping up with the best components and services that solve business problems. As an added bonus, multi-tenant platforms in the cloud will also begin to provide industry comparisons, so that companies can finally understand how they rank compared to their peers.

3) Incorporate AI and ML-powered Matching and Data Quality Improvement

Most organizations are not ready for any form of AI or ML to get deep customer insights, mainly because their data is in a poor, unreliable state. However, ML itself can be first used to help improve data quality (DQ) with better consistency, accuracy, and manageability. Incorporating ML helps uncover patterns, detect anomalies, and assist individuals such as data stewards by making their jobs more focused and efficient.

Whether your organization is trying to achieve 360-degree views of your customers, products or suppliers, this endeavor requires you to bring data from all internal, external, and third-party sources together. Blending all this data requires careful matching and merging.

Defining matching rule sets is a challenge, because it takes time and a deep understanding of data profiles. As the number of sources increases, and the format and data types grow, defining rules becomes more complicated. Simple rules-based matching may not be sufficient then, while data matching accuracy is often questionable. ML built into self-learning data platforms can help generate match rules automatically from data, and provide active learning training for data stewards.

Data stewards will be able to take a set or a sample of data, and run it through the matching rule sets and provide feedback about the data matching quality to the system as to which matches were good, and which were inadequate or inaccurate. With a single click, they can show the ML system how to treat the data and help system identify patterns to determine new match rules.

There are various other use cases where ML and rules-based data quality checks and inspections help continuously monitor the data quality, completeness and formatting. Using this information, systems can score and rank data appropriately. Systems can raise exceptions and send alerts to data owners and end users about the data quality exceptions such as incorrect phone numbers, inaccurate addresses or non-compliant profile information.

Intelligent recommendations can offer guided suggestions to identify new relationships, and suggest actions to influencers to improve the data quality scores. User-friendly dashboards continuously display the performance charts and graphs with improvement recommendations, where data quality trends of any segment of data or set of profiles can be monitored.

For example, you can compare the data quality of U.S. consumer records to the records for other countries, or do the same across product categories. You can search the data based on data quality scores, and if you’re running a campaign for a major product launch, you can eliminate profiles with low-quality scores. Real-time recalculations of data quality using ML provides immediate insights into the quality and recommended actions to improve the data.

4) Invest in an Open Ecosystem of Technologies

Data analytics has become an indispensable feature of successful companies across all industries. This reality dictates that companies invest heavily in data analytics to remain competitive and profitable. An open ecosystem that allows you to choose and partner with the technologies and domain experts of your choice is critical to getting the most out of a still young and evolving AI/ML landscape.

Getting locked into a single vendor, delivering siloed disparate data management and advanced analytics tools may further complicate an already fragmented data management strategy. Give yourself the openness and flexibility of your partners of choice to meet your business needs, so that you can quickly harvest the benefits of the latest AI and ML technologies.

5) Build Data-driven Applications with Embedded Analytics

Enterprise data-driven applications are built to infuse data from multiple enterprise sources and associate all the relevant and related transactions around the product or customer business entity. Marketing, e-commerce, customer service and sales teams access a consumer-grade interface with out-of-the-box analytics and reports to generate insights about top customers, top products, and top channels to make business decisions faster.

ML and predictive analytics incorporated into such data-driven applications can suggest next-best-actions for sending relevant and timely information to customers, and finding opportunities for up-sell and cross-sell. Insights like churn propensity, lifetime value, preferences and abandonment rates can be delivered to relevant teams, along with recommended actions to capitalize on this information.

Analytics and guided suggestions, when delivered within operational applications, are much more powerful and actionable than those presented in isolated, disconnected reports or dashboards.

6) Improve Data, Operations and Algorithms Continuously

Self-learning data organizations require that data first be made reliable, and connected with the rest of the enterprise, so that it can be trusted for use in critical business initiatives.

The role of ML is growing in business, helping make better sense of data and assisting to make decisions by processing volumes of information coming from a large number of sources. ML not only helps determine and improve data quality, but also delivers relevant insights and intelligent recommended actions for data quality and operational improvements, as you continue building a self-learning data organization.

For critical business initiatives such as getting deeper customer insights, a self-learning data platform with built-in ML allows organizations to score data quality for customer profile accuracy and the confidence level of derived attributes such as churn propensity or channel preference. Self-learning data platforms offer reliable data, relevant insights, and recommended actions through an ML-assisted closed-loop for both analytical intelligence and operational execution.

You will see that leveraging quantifiable business benefits from AI and ML is not at all complicated. Begin by choosing the right technology partners and organizing data into a trusted foundation. Move on to accelerate your business with ML-assisted improvement in data matching and merging. At the next step, opt for a platform to derive relevant insights and recommended actions with built-in ML-powered analytics.

Finally, progress to become a self-learning enterprise with continuous monitoring and improved data, insights, and actions to target unexplored market opportunities and achieve operational excellence at the best ROI.

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