Are you getting the full value from your open source usage?
This short self-assessment’s goal is to help identify areas where you could benefit from using a third-party support provider for your enterprise’s growing use of open source software. In July 2019, IBM commissioned Forrester Consulting to evaluate open source usage and the need for support. Using the study data, this assessment will yield customized results and recommendations based on your responses and should take no more than five minutes to complete.
1. Please select all that apply:
Which of the following classes of open source software tools/frameworks does your team use today?
2. Please select the best option:
What is the biggest support challenge you face when using open source technologies?
3. Please indicate your level of agreement with the following statement:
“Our organization lacks a mature governance strategy for open source technologies.”
4. Please select the option that best matches your current support model:
“What form of open source support do you most often leverage?”
Recommendation for Question #1
Recommendation for Question #2
Recommendation for Question #3
Recommendation for Question #4
Your score means you are most likely just beginning to scale AI and ML at your organization. Now is the time to make AI and ML a priority for your data and analytics investments: 80% of firms that are advanced with their AI/ML readiness say that AI and ML will be the most important factor in their business competitiveness in three years. While your data science team is likely small, here are some tactical recommendations to drive business value and get the most out of your initial AI and ML investments.
- Create a data science center of excellence. A center of excellence helps to democratize AI and ML in your organization and standardizes data practices to help get more high-quality models into production. Fifty-one percent of your intermediate and 71% of your advanced competitors have already implemented or are expanding their COE, while just 19% of your beginner peers have done so. Creating a COE is the fastest way to get your AI/ML practice off the ground.
- Encourage cooperation between data science, IT, and the business. Critical to success moving AI into the business is to foster collaboration and trust between your data science team building and implementing AI/ML models and the users trying to take those data insights and implement changes in the business. Nearly half of your intermediate competitors and two-thirds of advanced firms are undertaking initiatives to encourage cooperation between these often-siloed groups.
- Democratize AI and ML with automated machine learning and easy-to-use platforms. Most of your beginner peers have already made initial investments into machine learning and customer analytics platforms, so if you have not, that should be your first step. The next step many are taking is investing in automation platforms for ML. Automation is especially important to smaller data science teams because it allows data workers to easily create and implement new models and increases the productivity of your limited data science resources.
- Invest in technology to close the insights-to-action gap. The stage of the AI to ML lifecycle where your beginner peers encounter by far the most challenges is turning the insights from AL/ML initiatives into actionable changes to the business. To help close this insights-to-action gap, your more advanced competitors are increasing investment in new technologies like decision management and optimization and simulation solutions to find the best combination of business actions based on inputs from AI and ML models.
Your score means you are well underway with scaling AI and ML to drive business value at your organization. You are likely still growing your data science talent, so here are some tactical recommendations to drive business value and get the most out of your initial AI and ML investments.
- Focus on expanding business use cases. As you scale AI and ML into different areas of the business, elevate your use cases from those focused on driving efficiency to those driving business value. Your more advanced competitors are focused on use cases that improve the customer experience they can deliver, improve strategic decision making, and develop new revenue streams.
- Invest in technology to close the insights-to-action gap. To help close the insights-to-action gap, your more advanced competitors are increasing investment in new technologies like decision management and optimization and simulation solutions to find the best combination of business actions based on inputs from AI and ML models.
- Invest in ModelOps solutions to overcome model development and maintenance challenges. Your AI/ML projects may be underperforming due to challenges maintaining those models to ensure their accuracy. Your intermediate peers say model development and maintenance is the most difficult phase of the AI/ML lifecycle. ML operations solutions are designed to help both accelerate the deployment of AI and ML models and monitor them in production, making it easier to retrain them when conditions change.
- Expand your data collaboration initiatives. Advanced AI/ML firms further expand and democratize AI by creating data science centers of excellence and undertaking initiatives designed to foster collaboration both within your analytics teams and across the business and IT. Sixty-nine percent of your advanced competitors have already implemented or are expanding their COE, so if you do not have one, this is the time to act.
Congratulations, your score means that your AI and ML practice is thriving today. You are ahead of the curve on scaling AI and ML through your organization and driving business value with your initiatives. This is no time to rest on your laurels, however. Your advanced peers believe AI and ML is the most important factor to being competitive in the future and are investing with this mindset: 60% of your peers say they will invest at least $5M in AI/ML platforms in the next year. To continue to grow your practice effectively and improve your business results you should:
- Expand AI’s influence and boost productivity with automation. Tools like autoAI can help turn data workers into de facto data scientists with capabilities that automate feature engineering, model selection, parameter tuning, and model deployment. Your advanced peers see automation for AI and ML as by far the most valuable features of AI/ML platforms. These features not only allow data workers to quickly and easily develop AI/ML models but lets your precious data science resources work more productively and work on higher-value use cases.
- Drive trust in AI with governance and transparency. Drive trust and reduce risk while also improving business outcomes by investing in solutions for tracking model lineage, monitoring your models for data drift, and declining accuracy, as well as new techniques for providing explainability, bias detection, and mitigation features. This is another top feature for advanced firms to get the most value from their AI/ML platforms.
- Invest in ModelOps solutions to manage models at scale. Implement platforms with ModelOps capabilities that enable you to take models developed across a wide range of ML tools and frameworks, rapidly deploy them across hybrid environments (on-premises, private cloud, and multiple public clouds), retrain, and redeploy models as part of a continuous improvement process.
Want more information?
Ú Read the eBook, "Support Solutions For The Open Source Environment" to learn ways to manage the increased complexity of your open source software ecosystem.
Ú Watch our webinar that includes key data from the Forrester Commissioned Study to learn more about open source support solutions and overcoming challenges in open source environments.
Methodology, Disclaimers and Disclosures
In this study, Forrester conducted an online survey of 263 decision makers in the Canada, the US, the UK, Denmark, Spain, Italy, France, Russia, Germany, Australia, China, Japan, South Kora, and New Zealand to evaluate the current state of their technology and operational strategies. The study was completed in September 2019.
Although great care has been taken to ensure the accuracy and completeness of this assessment, IBM and Forrester are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein.
This interactive tool is commissioned by IBM and delivered by Forrester Consulting.