Artificial intelligence (AI) and machine learning (ML) have become omnipresent in our personal lives. The same is true in the business world. Data integration can help businesses take their AI applications to the next level. In many respects, AI is now a mature business technology. Consequently, it’s no longer the sole province of pioneering companies.
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Artificial intelligence (AI) and machine learning (ML) have become omnipresent in our personal lives. The same is true in the business world. Data integration can help businesses take their AI applications to the next level.
In many respects, AI is now a mature business technology. Consequently, it’s no longer the sole province of pioneering companies. Not only have advanced analytics powered by ML become popular. Chatbots responding to customer requests through a Natural Language Processing (NLP) AI are also on the rise.
However, many are leaving AI’s full potential untapped.
More than anything, AI needs data. ML algorithms ingest large data sets for training. AI is at its best when it extracts actionable information from a myriad of data points that no human could conceivably make sense of.
Businesses collect ever-larger amounts of data. Yet, most fail to harness all the data they have for their AI applications.
In 2021, a massive section of business operations happens in apps. These are now usually cloud services by Software as a Service (SaaS) companies. There are accounting platforms like FreshBooks. Customer Relationship Management (CRM) systems like Salesforce. Marketing powerhouses like HubSpot.
According to recent statistics, the average business uses 73 different apps. Furthermore, employees conduct over 80% of their work in the cloud. In 2020, the pandemic additionally magnified these trends.
Enormous amounts of data live on each of these platforms. With integrations, these services can take advantage of each other’s data. This enables countless business processes to take place in the cloud, from marketing to financial reporting.
Yet few businesses take advantage of all the possible integrations to unify their data and put it at the disposal of AI and ML.
Having a data integration strategy matters. Here’s why, what the challenges are, and how your business can benefit.
Why Strive for Data Integration? To Create and Harness Synergy.
In business, AI has three central tasks: process automation, generating-depth analyses, and engaging customers and team members.
For all this, data from connected business platforms is invaluable.
Let’s take a core office technology as an example: business phone services. Modern business telephony is cloud-based, thanks to Voice over Internet Protocol (VoIP) technology. For businesses, this has countless advantages, some more obvious than others. VoIP has enabled multiple communication channels, from video conferencing to live chat.
But VoIP business telephony also enables AI applications.
For example, NLP AIs can transcribe voice interactions in real-time. This can be applied to every single conversation between company representatives and customers. Then, ML algorithms can run advanced analytics such as sentiment analysis on the transcripts. The result? Company representatives can receive an instant evaluation of their performance. They can go over the conversation and learn.
But by integrating with other platforms, this kind of AI-generated data becomes even more useful. Connect your cloud business phone system with a CRM, and you can add every transcript to a customer profile. This immediately boosts possibilities for personalization. In turn, this can increase your chances of conversion or retention.
You can also integrate the data into a personnel management platform. For instance, sentiment analyses of a company representative’s conversations might be frequently negative. You’ll see this in their performance evaluation. Then, you can tackle the problem. Targeted training could, for example, showcase positive examples from other transcripts.
Or imagine you’re using sentiment analysis algorithms to sift through online reviews. By itself, this will give you valuable insights into customer attitudes and expectations. But integrating it with data from your digital marketing platform, and you can go much further. AI can then correlate email campaigns or social media strategies with sentiment trends in online reviews.
Getting Crucial Information in Real-Time
Speed is another core advantage of integrating data to boost AI performance. Integration increases the amount of information you can access. What’s more, access becomes easier and much faster.
Let’s go back to the business telephony example. The transcripts from customer calls are in your CRM. Imagine someone calling with a problem they were having with one of your products. A few days later, the same customer calls again. Now, the AI assistant in your business phone service pulls the relevant CRM records. Before your representative even picks up, they get a full run-down of this customer’s case: How they’ve described their problem. Personal information from demographics to billing history. And even what conversational strategies in the last call worked best.
All this saves your customer from having to give a long-form recap of their problem. For one, this reduces frustration on their part. For another, it also allows your representative to address them in a personalized manner. Overall, the exchange will be more efficient and satisfactory for both parties. Your business saves resources and increases customer satisfaction at the same time. All thanks to AI integration.
Generate Positive Feedback Loops
A final significant advantage of business app data integration for AI is positive feedback loops.
This means that by integrating platforms, you generate new data. On this basis, you can refine your strategies and grow your platforms. Leading to more data.
Take chatbots as an example. These days, countless companies use them in their online stores and on their websites. They are powered by NLP AIs. Their usefulness hinges on whether they can provide relevant information to customers.
For this, context is crucial. Customer behavior can provide that context. By supplying chatbot AIs with behavioral analytics, you can increase their usefulness.
For this, you can feed customer data from e-commerce and website analytics platforms and feed it to an AI. Have the AI find patterns and use these patterns to train your chatbot. On this basis, it will give suggestions and make recommendations to customers. Then, track the further behavior of these customers. It will give you even more data – to refine your chatbot.
This feedback training between chatbot AIs and e-commerce analytics helps increase communication efficiency. And seriously boost customer experience and thus retention and sales.
The Challenges of Data Integration
As the examples above make clear, data integration entails countless benefits for AI. But it also poses several challenges in its implementation.
First, businesses looking to integrate data face the hurdle of siloed technologies. Data silos are inaccessible for ML algorithm training. Nor can trained models analyze their contents for decision-making.
Second, the readability of data is a central issue. Many platforms have idiosyncratic data structures and lack metadata or context. They might even save files in proprietary formats.
Third, the trustworthiness of data is often in question. A study in the Harvard Business Review has highlighted this. In it, almost 50% of companies admit to data integrity problems. Issues with false, inconsistent, or stale data are frequent. And even more troubling, more than half of enterprise data fails to provide value altogether, due to siloing.
There are several avenues businesses can take to avoid these pitfalls and overcome these challenges to effectively harness integrated data for AI. The first of these is data mapping to establish a single source of truth.
Data Mapping and Single Source of Truth
Strictly speaking, data mapping is a subfield of data integration. It describes the process of connecting a data snippet in one system to a corresponding item in another. For example: Mapping the “name” field in a CRM record to the “caller ID” field in a business phone system.
The goal of this is to unify data while maintaining its integrity.
Crucially, this also helps businesses create single sources of truth. This refers to a single master record, which can boast high accuracy and sees frequent updates. Other systems will derive their data from here.
This doesn’t mean that all data needs to be stored in a single record. But for each item, only one ultimate source should exist. Especially highly sensitive data – like names, addresses, or social security numbers – should be treated like this. This makes it much easier to control and audit how this information is handled while still harnessing its value.
The good news is that modern AI-based cataloging tools can maintain such master records. They can parse information from all your platforms and deduce credible metadata. They can also automate data cleaning and highlight inconsistencies. This is a crucial first step in dealing with data complexity.
Dealing with Data Complexity
As mentioned above, data readability is a major hurdle to overcome. At its core, that means dealing with data complexity.
More and more raw data is available. This raw data is available in countless different formats. These formats range from social media posts over e-commerce statistics to server logs.
In addition, context is lacking for a lot of this information, especially natural language data. Take a thread of social media posts, which may include abbreviations and typos. Many statements might not make sense without the accompanying emoji, GIFs, or videos.
Plus, all this information is scattered across an ever-growing number of platforms. And these platforms, in turn, may have dozens, if not hundreds, of third-party integrations.
Using AI to Overcome Data Integration Challenges
The good news is that AI systems are becoming better and better at handling these challenges. This means you can use AI to generate input data for AI.
First off, AI can conduct data mapping automatically with precision and speed. In addition, the mapping mechanism can weed out issues such as duplicates and missing values. This helps maintain data integrity and speeds up data unification.
Furthermore, AI makes it possible to automate regular exports of data siloed in various places to central data hubs. It can infer data structures and parse uncommon formats. And crucially, it can propagate and consolidate data across systems, drawing from a single source of truth.
Recently, AI and ML algorithms have become adept at parsing situational context. If the schema structure for the incoming data is unknown or only partially known, they will either judge on a pattern basis, or parse the content to decipher a schema.
AI-Assisted Integration or Self-Service APIs?
One crucial question of data integration remains, though. How exactly can a business go about it?
It’s easy to say that AI can do this and that, without specifying exactly how it does so.
Generally, there are three ways in which businesses can approach data integration.
First, enterprise-scale businesses probably have the resources to hire and task data analysts with establishing a system of integration and analysis. They have the required knowledge in coding and data mining to set up a system that is custom-tailored to their employers’ specific needs. With the right access to the administrative space of various business apps, little should stand in the way of success.
Second, you can opt for self-service analytics platforms. A growing number of platforms aims to make analytics easily available for non-specialists. Many of these come in the form of cloud APIs. Their aim is to democratize AI analytics. Following this philosophy, some platforms even offer chatbot-like features. These help you create an integration procedure conversationally.
And finally, you can – at the very least – harness the integrations that are already at your disposal. Many major business apps already offer native integrations with each other. Even more, they are compatible with bridges such as Zapier. In many cases, taking the time to explore the options of integrations that you have in your existing app ecosystem can already pay off big time. Prioritize platforms that offer AI analytics and automation capabilities, and then proceed to map out the nexus of available integrations.
Data integration is a challenge. But it’s a challenge worth tackling to reap the rewards.
Harnessing all the data at your business’ disposal is vital in 2021. It will help you understand your customers better, meet their needs, and increase your sales.
The best way to go about this is to use AI to improve AI through data integration.
You can either manually explore your existing integration options, find an easy-to-handle cloud API, or approach the task with the help of a professional analyst.
Putting the required systems and automated processes in place will take some time and effort. At the end of the day, it will be worth it.
You will be able to boost the quality of your algorithms, establish positive feedback loops, and gain insights at an unprecedented level. With decisions based on the insights, you’ll be able to steer your business through the times to come.
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