This post will be of use to all business owners who are eager to implement AI technology but not so sure where to start. We will discuss key challenges related to data management and how to prepare you 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 take into account, and what design principles to apply to data architecture.
Whatever business industry we name, it certainly deals with a lot of data on a daily basis. 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, most of the time, the answer to both of these questions is no, the work with data still poses a lot of challenges that only AI technology can solve once and for all.
Hundreds or even thousands of companies all over the world 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 the step of correctly preparing for AI integration.
Here is what happens – the companies simply forget to organize and structure all their data, so that AI technology can really optimize its processing and use. To help businesses get AI ready we did some in-depth research, 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 the data IQ, architecture, quality, and what key principles you need to follow.
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 terms of data processing, it brings new challenges. While for Big Data it is enough to collect information and on its basis identify the key problems for the company, AI requires well-defined objectives and uses data as a strategic tool.
Before implementing AI technology you need to find out how and where all generated data will be located, and how it will be spread across all your business systems. To make the right decisions, you need an experienced team of software architects and engineers. 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 data pain points
The Harvard Business Review has published an article dedicated to data infrastructure. That 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 pain points related to data in your business. It can be one holistic problem, or dozens of simple separate bottlenecks that create additional problems and do not let you use the available data to the fullest. Pain points may help you dig a bit deeper and find out 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 issues with data scalability and security, while the other company may be unable to perform holistic data analysis and ensure scalability.
Everything depends on your business specifics and sometimes the bottlenecks are not so obvious. So apart from your in-house team of technicians, you may also need help from an experienced and professional team like GBKSOFT.
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 in the right form and if all its components are taken into account.
According to the research provided by IBIMA publishing, companies must assess and score their data IQ. It is possible to do this by using specific methodologies for data assessment. For example, many companies these days tend to use the Six Sigma approach that was previously used to identify product quality. Information can be seen as a product, so 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 real intelligence level of data in your company and its readiness for introduction of complex AI analytical systems.
Still lack reliable business intelligence tools that work with data?
GBKSOFT team can develop solutions that will fit your business needs perfectly and help you with the data collection and analytics.
Determine new data metrics
AI-powered solutions require whole sets of metrics. This means that the data should not only be collected in one place, it should be analytically ready for the creation of a data model that will be transformed in a data platform. You may be wondering who is responsible for data preparation and platform engineering. Usually there are data scientists who are involved in that process.
If your goal is to incorporate an AI system for better work with data, then you need to get prepared for collecting a huge 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 embedded in systems that your company uses! Unfortunately applications and data transformation have been viewed as separate initiatives. And for getting AI-ready it is crucial to make them work together without any gaps.
To get started with data metrics development, first think about your business objectives and value. 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, it is convenient, safe and offers way 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 on the market (i.e. Amazon, IBM, Microsoft, Google, etc.) all you need is a right strategy and skillful developers team to complete the migration.
We have already covered in detail how companies can choose and execute the right cloud strategy. If you are interested in this topic, then check that post right away. The GBKSOFT team has great experts who can help you with cloud migration, they will do it in the most efficient way by ensuring cloud security, compliance, management and they will check if the cloud architecture matches your business needs and requirements.
You may be wondering why we included this step in the must-have list of AI ready companies. The thing is that to adopt AI on enterprise level, you need to have an integrated and easily manageable data foundation.
By introducing AI technology into your business model you attempt to resolve more global challenges that consist of 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 exactly what migration to the cloud offers.
If you store your data in the cloud it means that 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:
On top of that, you get an opportunity to examine your data quality that determines how well the AI powered system will be trained and tested. The AI system can use the data that is meaningful, comprehensive, relevant and correlated 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.
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Choose right CTO & reliable development partner
We could not emphasize enough how important it is to assign complex AI projects to very experienced and knowledgeable CTOs. This specialist should have a deep understanding of AI initiatives, data management and have a design thinking approach. What’s also important is to find a person that understands not only the technical part of digital transformation, but also knows your business, its pros, cons, current demands, and is able to introduce relevant know-hows.
A strong CTO who knows your business ins and outs can assist in selecting the right technologies and become your primary decision maker if you outsource your AI-powered software development.
As to the developers team, when selecting one, don’t forget to check if they have any prior experience in building AI systems in your industry, and if they are skilled enough in cloud migration. After all, an AI system is not a standalone tool, it works together with many other solutions collecting and analyzing the data. So everything should be interconnected and well set up.
Perhaps some of your systems are outdated and they will need a refactoring and code review. So find a team like GBKSOFT that specializes in digital transformation of businesses and high technologies including AI, ML and IoT.
Want to start preparing your company for AI integration?
Contact our experts that will help you by undertaking holistic research, then suggesting and developing the most suitable AI solutions for your business.
How to make data architecture AI-ready?
AI really turns data management in companies up to a whole new level. However, to achieve impressive hyper personalization of data and make real-time data-driven decisions, you need to take good care of your data architecture. The first step should be migration to the cloud since it does not restrict different data models and allows applying AI technology in various forms (i.e. 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:
To wrap it up
Needless to say that AI is the technology of the future that will reshape numerous business industries and make them rethink the way they function and work with data. With years, more and more companies will transfer 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 your time, money and become a less smart company that constantly struggles with information gibberish. Before implementing new AI solutions, inspect your data carefully, find its pain points and strengths, and complete 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.
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