8 common challenges of AI adoption and how to solve them in one month
Apr 25, 2023
The adoption of AI is at an all-time high. According to the IBM Global AI Adoption Index 2023, a survey conducted on over 7500 businesses reveals that 35% of companies already use AI in their everyday work (which is a 4 point increase compared to 2021) while 45% of them are still investigating AI and preparing a data strategy.
Before your organization adopts AI, it needs a data strategy. Yet, only 32% of companies have it, which is concerning. With so much data collected, it’s borderline dangerous for your business not to have a carefully planned approach.
A data strategy is a plan for how your organization will collect, manage, analyze, and use data to support business goals.
The current state of the organization’s data (quality, availability, and accessibility of data),
available technologies, and
skills and capabilities of the data team (and other teams in the company, too).
It should also consider future requirements and the changing landscape of data and technology, such as the growth of big data, the increasing importance of data privacy, and the emergence of new data technologies.
In simpler terms, the key answer that a careful data strategy provides is how to ensure that data is managed in a way that provides you with actual business value, while causing no technical or legal headaches.
Easier said than done: implementing data strategy imposes potent challenges
Even though you’ve prepared everything, handling data according to strategy and preparing it for analytics and AI/ML causes serious bottlenecks. It’s a series of problems many companies encounter, according to our insights on the ground.
Many challenges revolve either around data quality and data management processes or data security and technical skills. There are also integration, connectivity, and visualization issues.
We’ve categorized them in 8 crucial categories.
1. Collecting data from many sources – efficiently
Collecting large amounts of unsorted data at high velocity from many sources requires many integrations. It means you have to assemble multiple data sources in one place to have a real overview of business, which is time-consuming – more than you’d love to.
Business leaders need information, and they need them fast. Wasting resources on checking multiple sources and tools, doing manual analysis, deriving insights… It doesn’t sound very productive in this era.
2. Connecting data silos and storing data
Real insights require storing historical data. Yet, not all data is ready for use. Every department has its own silo. Bringing it to one infrastructure for analysis requires a strong data storage layer.
It needs to support scalability while ensuring cost efficiency and performant data access.
3. Organizing and processing data while avoiding costly errors
The stored data needs to be processed in order to be used for a range of tasks, such as data analytics and machine learning. That includes data cleansing, transforming, aggregating, feature extracting, and much more.
All this takes significant time, and it’s prone to human error.
4. Cost and benefit analysis of suitable infrastructure for storing and processing data
If you have big data sets, storing and operating them on hardware just isn’t sustainable in terms of costs in the long run.
Cloud-based solutions offer scalable computing power, which can keep up with the ever-increasing size of data volume. However, connectivity (and thus performance), privacy and security, and latency remain the issue.
The problem lies in finding a subtle balance between cost-effectiveness and scalability of infrastructure.
5. Keeping up with growth
Business is growing – and with it, the data too. It’s worthwhile keeping in mind that data infrastructure and teams need to scale together with them. Company switches from 10 to 100 reports per day in the blink of an eye.
But data goes through countless transformations. If a company isn’t involved in defining security and governance measures from the beginning, it is much harder to steer them later. Especially if you consider the fact that 63% of surveyed companies with data governance still struggle with adoption and spend big budgets on training.
7. Getting value from data
When provided with unified storage, processing, and access to all data regardless of their nature, we can:
Use data in business intelligence, visualization, and reporting tools, in order to derive deep insights,
find hidden patterns and conclusions to enable data-driven business, and
monetize collected data. You can sell non-proprietary data, offer data as a service and much more to fuel the revenue streams.
The key issue in this area is providing non-technical personnel with easy-to-understand data. Even the best infrastructure remains underutilized if only one department in the building can decipher its outputs.
Aside from numbers and reports to analyze, or monetizing insights – having a unified place for data can provide you with additional value, e.g. better risk management, efficient talent management, increased operational efficiency…
There are few limitations to what the data can tell you. However, getting them in a streamlined way, like from a factory line – that’s a tough task to handle.
AIDA solves these challenges – and much more
After many years in the market, we can safely assume that the success rate in solving these problems relies on three factors:
Capabilities to orchestrate processes
Making suitable compromises in terms of resources
Willingness to endure implementation periods
That’s why we’ve created AIDA – an AI-ready Data Platform solution that enables collecting heterogeneous data from various data sources, storing and processing it in a scalable and cost-effective way.
It allows you to orchestrate better, avoid bad compromises in terms of capabilities and resources, and implement faster.
Significantly decreases time to value. By the end of the first month of development, AIDA enables you to find insights from data.
Helps you reduce expenses by opting for serverless services rather than on-demand, selecting cluster instances that are appropriate to the use-case and applying auto-scaling for unexpected load.
This design allows for adjustments to different sizes and for large amounts of data and processing to be handled without going over budget. It is a cloud-agnostic solution, possible to implement on-premise.
It works as a central data repository. It allows you to collect, store, and process data in one place.
Integration with data sources requires minimum modification of existing systems/applications.
Metadata allows airtight data governance. AIDA is GDPR compliant and management of the roles for accessing the data can be easily done.
Its modular structure and approach make it possible to start with the essential components only and to build up gradually. That allows you to use this platform as you need.
It provides you with a unified access point – thus making the pre-processed and structured data easily accessible for both everyday business use, business intelligence, and AI experiments (making it AI-ready).
Technically, AIDA brings together the most helpful aspects of the two most commonly used data infrastructure systems – Data Warehouse and Data Lake.
From a Data Lake perspective, it allows for collecting and working with both structured and unstructured data in various data formats, and relies on scalable and cost-effective data storage.
From a Data Warehouse perspective, AIDA offers an unified and structured access to all data. This provides us with a Data Lakehouse concept, on top of which AIDA offers features for applications of ML/AI, making it possible to extract additional value from the data through new insights and implementation of automated decisions.
From a high-level perspective, AIDA comprises the following layers: ingestion, storage, processing, and access. There are also two additional supporting layers: governance and infrastructure.
Data strategy doesn’t have to be the most painful process in the company
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