Your next steps to drive business value through advanced analytics innovation

Intel Corporation explores how busy IT teams can drive their analytics strategy forward in support of business goals.

It’s an exciting time to be working in IT. As the volume and complexity of the data held by your business grows, the potential for the IT team to play a critical role in future success grows with it. As enterprises in all sectors become more data-centric, your expertise in managing, securing, and using data to inform and drive business outcomes will remain in high demand. And as that data and its multiple sources become more complex, so too will the challenge you face in responding to demands from the business.

At Intel, we work with dedicated IT teams around the world who are tasked with ensuring the technology resources within the business can keep up with the faster pace and greater pressures of today’s digital economy. Many are turning to analytics to help with this, and seeing great results. However, whether you’re just starting to experiment with predictive analytics or delving into complex machine learning algorithms, it’s important to think about what’s next. Your advanced analytics capabilities need to evolve in line with your data environment and your business strategy.

My team within Intel is committed to driving industry-wide innovation using cutting-edge machine learning and big data technologies, to deliver advanced analytics solutions. I’ve recently published a white paper outlining a range of possible use cases for artificial intelligence, machine learning, and other analytics practices, as well as sharing some of the learnings and best practices that we’ve developed through our own experience. Here are some areas to consider when planning your own advanced analytics strategy:

1. Get the right team structure

A strong advanced analytics team should include three main roles: data scientists/analysts, product managers, and data engineers. Each role has a critical and distinct part to play at each stage of an analytics project so they must be clearly defined, but they must also be closely integrated so they can work collaboratively as part of a united team.

2. Prioritize talent acquisition and retention

The jobs marketplace is increasingly competitive and filling science, technology, engineering, and mathematics (STEM) roles can be notoriously tricky. As a leader of the group, you need to invest a lot of effort yourself and work with your HR team to ensure that you have an effective plan in place for not only finding and recruiting new people for your analytics teams but also for nurturing and developing them throughout their career with you.

3. Encourage innovation

Advanced analytics is great for driving new business outcome improvements and shining a light on new opportunities that may otherwise have gone unnoticed. However, in order to make the most of these opportunities, it may be necessary to innovate, to push the company outside its comfort zone. While agile start-up companies may thrive on this approach, larger enterprises often struggle to overcome ingrained processes and complex hierarchies. It’s important for senior leadership to show those using advanced analytics that they have the freedom to innovate and be creative, and that risk taking – when based on detailed analysis and insight – is a key for success.

4. Demonstrate value

Intel 5-6-10 Rule Graphic
The Intel 5/6/10 Rule

Investments in technology can be viewed with suspicion by finance and business unit (BU) colleagues if they do not understand the value that they will return. It’s therefore important to encourage support for investment in and development of advanced analytics solutions early on by showing their positive impact on the bottom line. Have a small team focus on a specific business issue for a short period (say, six months) and use their tangible results as a proof point. Once one BU sees another getting results, they’ll be much more interested in joining in. At Intel, we apply the 5/6/10 rule, which stands for five skilled people assigned for six months to work on a project that yields a value of $10M.

5. Reuse

Think about how to optimize the output and business value of every advanced analytics project you undertake. For example; can you adapt a solution you’re using internally to turn it into a service for your customers as well? Can you replicate one initiative in another business area or geography? Plan carefully to ensure the initiatives you’re focusing on are the ones that will deliver the most value for you, your colleagues, and customers over time.

6. Stay in touch

The world of machine learning and big data is evolving rapidly and even the most innovative company can come off track if it doesn’t keep tabs on what its customers need and how its industry is changing. Ensure your advanced analytics initiatives take outside perspectives into account to balance internal views and maintain relevance. Foster a strong relationship with the industry by encouraging team members to attend conferences and events, engage with local universities, and participate in research projects.

Learn more about how advanced analytics can help you transform your business, and what you can do to make it happen, by reading this new white paper from Intel.