It’s usually not a good idea to make business decisions based on gut sentiments, and it’s much less likely to be the most innovative way to spend your money. However, there is a more effective approach.
We’ll look at two crucial areas in this article: data strategy and AI strategy, which can help you make wiser, more cost-effective decisions.There are some parallels, but there are some significant distinctions as well. Let’s start with the commonalities between the two approaches.
The similarities and differences between data and ai strategy
It would help if you always began with your overarching business strategy, regardless of which tactics you are interested in. This entails learning about its stakeholders’ vision and direction to align the data and AI technologies you want to develop.
A discovery phase should be completed before either method is developed. This allows you to conduct a gap analysis, which compares your company to where you want it to be. You can then sort the gaps by their worth. This is the map you’ll need to chart your course to success.
Every projects’ success is dependent on discovery and analysis. We’re used to doing this kind of work when our clients haven’t taken these steps yet.
Let’s have a look at the data strategy next.
What Is Data strategy?
Keep in mind that data strategy and AI strategy are fundamentally distinct approaches to answering the same problems. Data strategy is all about how you can use data to improve your business, and it includes issues like:
- How can we make better strategic judgments by utilizing data?
- How can we make better use of data in our day-to-day operations?
- What skills and culture do we need in our company to take advantage of these opportunities?
- What kind of governance is required to make this function in the business consistently?
- What data does my company have access to, and how can I be confident in its accuracy?
- What technology do I need to make this happen?
Any company that wants to be a “data-driven business” needs a data strategy. While this is a well-known term, many people are unaware of what it implies in actuality. To put it another way, data-driven decision-making involves relying on numbers rather than intuition. The data-driven mindset must permeate the organization for this to operate, with all departments employing KPIs in their day-to-day operations. To achieve this, the organization must establish sufficient “data literacy,” which means that management must train and advise its employees.
To meet the needs of the business, data strategy must also take into account the architecture of technology. This implies that, given your company’s data, you’ll need to figure out what technology you’ll need to make the most of it so you can make the most significant business decisions.
This may entail the usage of data lakes, data warehouses, or other data storage, processing, and movement systems.
What Is An AI Strategy?
The goal of AI strategy is to figure out how you can use AI to benefit your business. You may believe that your AI strategy can only exist when a comprehensive data plan has been created. That isn’t correct.
We often say that relevant data is needed for good AI, and this is still true. However, no organization starts from scratch when it comes to data understanding. Therefore an AI technologies plan doesn’t always necessitate a solid data strategy first.
Having a robust data strategy in place, on the other hand, means you’ll understand things like data governance and data cleanliness, which makes the AI process much more manageable.
We use the following approach to data strategy:
- Examine all of your options.
- Score and then rank those prospects based on their ability to add value to the company.
- Determine the amount of effort required for each chance.
- Determine our level of confidence in our ability to deliver on each opportunity.
These factors, when considered collectively, enable us to assess the risks associated with each opportunity. We’d provide a low-risk, high-value, easy-to-implement solution that gives our clients the most bang for their dollars in a perfect world.
Defining a good AI strategy entails being able to provide clients with a unique and profitable service. Consider the following examples:
- Clustering: It is an AI technologies method that allows companies to discover and target certain client groups quickly. While traditional customer segmentation divides people into groups based on their demographics, AI technologies clusters divide people into groups based on their behavior, which is considerably more successful when matching them with the correct products, services, and messaging.
- Operational efficiency: AI systems may be taught to detect industrial processes flaws and do predictive maintenance, lowering failure rates and repair costs.
- More innovative products and services: the possibilities are evident in a large number of AI-enabled consumer solutions currently available, including Alexa, Siri, satnavs, automatic video captioning, automatic language translation, auto-identification of faces in images, and many others.
This is just the tip of the iceberg regarding what AI can do now and in the future. We believe that having a solid data strategy, followed by an AI plan, will be critical for organizations to maintain their market position and grow over the next decade.
Conclusion
We believe that solid strategies are critical, and we know from our clients’ experiences that those that establish robust plans win in the long run. To put it another way, it’s advisable to invest a small amount of money in getting your strategy correct before diving into a large-scale tech project.
With our help, you’ll be in a better position to become a more lucrative, data-driven company rather than one that relies on gut instinct. Gut instinct only gets you so far — data always prevails in the end!
So, if you’re interested in adopting data strategy into your company, get in touch with the ONPASSIVE team.
