Is Your Company AI-Ready? The Data Challenge

This post will be helpful to all business owners who are eager to implement AI technology but are unsure where to start. We will discuss key challenges related to data management and how to prepare your company for AI integration. You will discover why it is crucial to migrate to the cloud, how to measure your data IQ, what metrics to consider, and what design principles to apply to data architecture.

Whatever business industry we name, it deals with a lot of data daily. Various companies have apps, websites, smart devices, and complex technological systems that collect, process, store and analyze that data. But is that data used effectively in the end? Does it bring any value or advantages to your company?

Unfortunately, the answer to both of these questions is usually no. Working with data still poses many challenges that only AI technology can solve once and for all.

Hundreds or even thousands of companies worldwide invest a great deal of money in digital transformation and implementation of AI technology. And unfortunately, most of those initiatives fail, and all effort and finances go down the drain. One crucial thing that all those companies miss is correctly preparing for AI integration. 

Here is what happens: Companies simply forget to organize and structure all their data so that AI technology can optimize its processing and use. We did some in-depth research to help businesses get AI ready, and now we’d like to share some exclusive, crucial insights and aspects with you. Read on to discover the key things to tick off, such as what to do with data IQ, architecture, quality, and key principles.

Where should you start? 

Several years ago, Big Data started becoming a buzzword in the tech world, many companies initiated its adoption to bring their business to a whole new level and work more productively. And while for the intents and purposes of some businesses, Big Data can be enough, others still require new approaches and innovative technologies like AI. 

Although AI is more efficient in data processing, it brings new challenges. While for Big Data it is enough to collect information and identify the key problems for the company, AI requires well-defined objectives and uses data as a strategic tool.

Before implementing AI technology, you must find out how and where all generated data will be located, and how it will be spread across all your business systems. You need an experienced team of software architects and engineers to make the right decisions. On top of that, you need to focus on AI-based approaches and take necessary action. Let’s take a closer look at them.

Find out the data pain points 

The Harvard Business Review has published an article on data infrastructure. The article provides valuable advice on how to make your company AI-ready. 

The first thing that the companies should do is to build an ontology that will help them make sense and understand the data that flows through their companies and how it can be used effectively and to their business advantage.

Harvard Business Review

To build that ontology, you need to discover if there are any data-related pain points in your business. These can be one holistic problem or dozens of simple, separate bottlenecks that create additional problems and prevent you from using the available data to the fullest. Pain points may help you dig deeper and discover what is wrong with your data architecture and what should be changed before you consider AI.

The data pain points will differ between companies. For example, one company may experience data scalability and security issues, while the other company may be unable to perform holistic data analysis and ensure scalability. 

Everything depends on your business specifics; sometimes, the bottlenecks are not obvious. So, besides your in-house team of technicians, you may also need help from an experienced and professional team like Altamira.

Figure out your data IQ

We’ve mentioned several times that companies accumulate a lot of data, but unfortunately they often forget to take notice of the quality of that data. Poor information processing systems do not let you see the full picture and identify whether the data is appropriately formed and if all its components are considered. 

According to research provided by IBIMA Publishing, companies must assess and score their data IQ. This can be done using specific methodologies for data assessment. For example, many companies use the Six Sigma approach, which was previously used to identify product quality. Since information can be seen as a product, Six Sigma can be applied to assess and improve data IQ. 

The IQ dimensions help to define the key data quality metrics that should be improved. For example, if you are involved in the supply chain, you may perform a survey or interview of customers to identify if you are collecting and processing data of high quality. Here is how the responses of clients can be turned into data IQ dimensions:

Image source: IBIMA Publishing

The same strategy can be applied in other business industries as well. For example, healthcare institutions can initiate such interviews among employees and patients. Then identify what should be changed about data quality. Such an approach will help you identify the actual intelligence level of data in your company and its readiness to introduce complex AI analytical systems. 

Determine new data metrics

AI-powered solutions require whole sets of metrics. This means that the data should be collected in one place and analytically ready to create a data model that will be transformed into a data platform. You may be wondering who is responsible for data preparation and platform engineering. Usually, data scientists are involved in that process.

If your goal is to incorporate an AI system for better work with data, you need to prepare to collect a vast breadth of data. And you need to do this in a new way that will allow you to get and use only high-quality data corresponding to improved IQ requirements. 

What can help you collect all necessary data? Powerful and effective analytics are embedded in your company’s systems! Unfortunately, applications and data transformation have been viewed as separate initiatives. Making them work together without gaps is crucial for getting AI-ready.

First, think about your business objectives and value to get started with data metrics development. Measure your data to target them both. For example, if your goal is to ensure better visibility of overall company progress, then start collecting high-level data related to each department – their KPIs, performance review information, details related to finance, sales, etc. 

Move your data to the cloud

It is impossible to imagine modern companies working without cloud-based systems. After all, they are convenient, safe, and offer more diverse opportunities. Many businesses are either strongly considering and planning migration to the cloud or have already successfully undergone it. With so many reliable cloud providers (e.g., Amazon, IBM, Microsoft, Google, etc.), you only need the right strategy and a skilled developer team to complete the migration.

We have already discussed how companies can choose and execute the right cloud strategy. If you are interested in this topic, check that post immediately. The Altamira team has great experts who can help you with cloud migration. They will do it efficiently by ensuring cloud security, compliance, and management, and they will check whether the cloud architecture matches your business needs and requirements.

You may wonder why we included this step in the must-have list of AI-ready companies. The answer is that you need an integrated and easily manageable data foundation to adopt AI at the enterprise level.

By introducing AI technology into your business model, you attempt to resolve more global challenges with dozens of small ones. The AI strategy implies covering data issues faced by all departments, organizations and entities related to your company. This means that you need to store all your information in one place, provide access to it on many different levels, and be able to process it anytime. And this is precisely what migration to the cloud offers. 

If you store your data in the cloud, you have a centralized infrastructure that allows you to integrate new technologies, use the data across and outside your business, and scale your company without any major obstacles. Wondering how the data migration is performed? Then take a look at the video below explaining some key points:

In addition, you can examine your data quality, which determines how well the AI-powered system will be trained and tested. The AI system can use meaningful, comprehensive, relevant, and correlated data to the desired business objectives. With a cloud-based approach, it is easier to keep data quality under control and use AI power to the fullest.

Choose the right CTO & a reliable development partner. 

We could not emphasize enough the importance of assigning complex AI projects to very experienced and knowledgeable CTOs. This specialist should have a deep understanding of AI initiatives and data management and a design thinking approach. It is also essential to find a person who understands not only the technical part of digital transformation but also knows your business, its pros, cons, and current demands, and can introduce relevant know-how. 

If you outsource AI-powered software development, a strong CTO who knows your business can help you select the right technologies and become your primary decision-maker.

When selecting a developer team, remember to check whether they have any prior experience building AI systems in your industry and whether they are skilled enough in cloud migration. After all, an AI system is not a standalone tool; it works with many other solutions to collect and analyze data. So, everything should be interconnected and well-set up.

Perhaps some of your systems are outdated and need refactoring and code review. In that case, find a team like Altamira specialising in digital transformation of businesses and high-tech technologies, including AI, ML, and IoT.   

How to make data architecture AI-ready? 

AI elevates data management in companies to a whole new level. However, to achieve impressive hyper-personalization of data and make real-time data-driven decisions, you must take good care of your data architecture. The first step should be migrating to the cloud since it does not restrict different data models and allows the application of AI technology in various forms (e.g., machine learning of different types – supervised, reinforced, semi-supervised, etc.).

The second step is to apply design principles to your data architecture. We have collected 5 key ones for you. So, without further ado, let’s shed some light on them:

Ensure data scalability and flexibility.

Review how the data is used in your company now and whether too many steps are required before anything else can be applied. For example, if you need to apply a formula to a vast data set, can you do this without discussing the changes with the technical department? Can your solutions be updated without waiting for general system updates? Can you recover the data quickly in case of an accident, or is this a huge problem?

Manage metadata properly.

Unfortunately, many companies are not metadata driven. They tend to process and manage metadata not from the start when it is received. As a result, the companies cannot collect it and create proper data sets or even a library of them. So, it is more challenging for companies’ employees to find, access, and use that metadata to generate insights necessary for AI-powered solutions.

Take care of access to data layers.

If you still use an older data architecture, you likely allow free access only to one data layer: consumption. However, your technicians may not have access to raw or curated data, which is crucial if you want to see the whole business picture. You’ll lose necessary data elements if you don’t update your data architecture.

Change the point of data access.

AI solutions demand access to various data, such as logs, information collected from social media and sensors, transaction information, and more. If your data architecture cannot access and consume all this data (including different data structures) in real time, you may have limited AI functionality in the future.

Choose the right security model.

Many businesses still benefit from using cloud-based and on-premise services to create hybrid environments. It’s not that bad. However, if your goal is to become an AI-ready company, you need to pay special attention to data security, location, access, and consumption by end users. Therefore, different levels of security are required for various data sets. You need a holistic security approach to let AI systems use the data quickly and safely.

To wrap it up

Needless to say, AI is the technology of the future. It will reshape numerous business industries and make them rethink how they function and work with data. Over the years, more and more companies will transition from simple business intelligence tools to more advanced AI-powered ones. But before they do this, all business owners should pay special attention to preparing their companies and data for AI integration. 

Preparation is the only key to success. Otherwise, you will lose time and money and become a less-smart company constantly struggling with information gibberish. Before implementing new AI solutions, inspect your data carefully, identify its pain points and strengths, and complete the migration to the cloud. Then, ensure the highest possible level of security. Once your data architecture is updated and AI-ready, consider developing new smart solutions for your business.

FAQ