5 Red Flags to Consider When Integrating AI with BI

Today, AI is commonly regarded as the enabler that business intelligence has long lacked to achieve next-level commercial value. However, integrating AI into a current business intelligence platform is difficult, especially with a lack of business intelligence consulting services. 

Additionally, it might be precarious: AI has the potential to amplify significantly any almost imperceptible flaw into a far more remarkable — and detrimental — impact on downstream systems. For instance, there is no risk if you cannot sing in tune but merely use a tiny karaoke machine at home. This is not a significant issue if you have business intelligence service providers. However, imagine yourself in a massive stadium equipped with a multi-megawatt sound system. 

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With that amplification capability in mind, enterprises considering integrating AI into their business intelligence systems must be vigilant for red flags that can jeopardize even the most well-intentioned projects. 

The following are significant issues to avoid when merging AI with business intelligence: 

1. Absence of business use case

This should be the most obvious danger to avoid — and the most frequently encountered in existing AI solutions. As tempting as it may be to incorporate AI into your business intelligence service provider’s intelligence solution to keep up with your colleagues or competitors, the implications can be disastrous. If you invest millions of dollars automating labor performed by a single individual earning $60,000 per year, it may be tough to explain the ROI. The positive ROI is not immediately apparent in this case with business intelligence services. 

When soliciting input from business leaders regarding the viability of an AI-enabled business intelligence solution, begin the discussion with specific scenarios in which the scale and scope of AI could potentially address possibly the best gaps and result in a business value that exceeds the estimated expense. However, if the gaps are not well understood or the creation of substantial new value is not guaranteed, it becomes difficult to justify continuing. 

2. Inadequate training data

What next should you look out for? Assume you have a sound business case, then what could you be missing? To train AI via machine learning (ML), you will need sufficient data. How much data do you have? Does it have enough substance to be useful for training AI algorithms? Depending on the context, it could go in one of several different directions. In this case, Thomson Reuters needed almost two million news stories to build a Text Research Collection in 2009 for news categorization, clustering, and summarization. 

Once you’ve made it to this point, you should know by now that business intelligence service providers can determine what type of training data is appropriate for the specific use case. 

3. Lack of AI teacher 

If you have an exceptional data scientist on staff, this does not mean you already have an AI instructor. It’s one thing to code in R or Python and creates sophisticated analytical solutions; it’s quite another to find the appropriate data for AI training, package it properly for AI training, check the output continually, and assist AI along its learning path. 

An AI teacher is not simply a data scientist; it is a data scientist with a great deal of patience for the incremental machine learning process, a thorough understanding of the corporate environment and the problem at hand, and a keen awareness of the risk of bias introduction during the teaching process. 

AI teachers are a rare breed, as AI education is widely recognized to be at the nexus of artificial intelligence, neurology, and psychology – and they may be hard to come by at present with BI and analytics services. 

4. Inaccurate master data

In both artificial intelligence (AI) and traditional business intelligence (BI), master data (MD) is crucial. The better MD is when it has a greater level of maturity or definition. This can also be done in an AI system but not in a BI solution. 

The main application of AI is in preparation for business intelligence and AI. However, using AI in this manner is distinct from its original intent, and it is known as data preparation for BI and AI. 

How can we recognize the difference between mature and immature MD? First, consider the following: 

  1. The degree of assurance associated with MD Entity deduplication – should be close to 100 percent.
  2. The degree to which relationships are managed: For instance, within each MD entity class, “Company A is the parent company of Company B.” – Across many MD entity classes — for instance, “Company A-supplies-Part XYZ.” 
  3. No Consistency

The consistency of categorizations, classifications, and taxonomies is necessary by business intelligence services companies. For example, if the marketing department uses a different system for product classification than the finance department, the two systems must be accurate – and transparently – mapped to one another. 

If your AI implementation requires the use of complex text, such as free-form text or other material that does not have a pre-defined data model, then watch out next red flag, we discuss. 

5. There is no well-developed knowledge graph

A knowledge network comprises interconnected nodes and relationships between those nodes, much like a Wikipedia article. Imagine a machine-readable form with all definitions, classes, instances, relationships, and classifications. It would be a knowledge graph enabled by business intelligence service providers. Knowledge graphs encompass class-level attributes (information model) combined with instance and relationship data (information model and instance data), logical constraints, and behavioral rules. 

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The more Semantic Web standards are used, the less time the process of educating AI is estimated to take. The Extendibility of a knowledge graph is endless in terms of the many informative models, linkages, constraints, and so on. Additionally, knowledge graphs are also mergeable. Using only a data warehouse, a knowledge graph is essential for AI applications. 

AI solutions for unstructured data — where the AI uses the graph structure to analyze unstructured data in the same way that structured data is analyzed; AI storytelling solutions — where the analytical results are viewed as a story or narrative, rather than as tables or charts, thereby transforming business intelligence implementation services from an on-screen visualization supporting the table discussion to a participant in it 

While these perceived dangers may first appear to be more frightening, the threat of failure much outweighs any real risk. In addition, effective AI deployments have detailed plans to help remind them of how to do it well. 

With artificial intelligence, business intelligence has the potential to elevate from a tool that generates insights to a trusted participant in all real-world decisions. It is a competency gap – but be mindful of the risks. 

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