Real-world software applications must constantly evolve to remain relevant. This evolution occurs when developing new applications or adapting existing ones to meet new requirements, make corrections, or incorporate future functionality. Traditional methods of software quality control involve software quality models and continuous code inspection tools. These measures focus on directly assessing the quality of the software. However, there is a strong correlation and causation between the quality of the development process and the resulting software product. Therefore, improving the development process indirectly improves the software product, too. To achieve this, effective learning from past processes is necessary, often embraced through post mortem organizational learning. While qualitative evaluation of large artifacts is common, smaller quantitative changes captured by application lifecycle management are often overlooked. In addition to software metrics, these smaller changes can reveal complex phenomena related to project culture and management. Leveraging these changes can help detect and address such complex issues. Software evolution was previously measured by the size of changes, but the lack of consensus on a reliable and versatile quantification method prevents its use as a dependable metric. Different size classifications fail to reliably describe the nature of evolution. While application lifecycle management data is rich, identifying which artifacts can model detrimental managerial practices remains uncertain. Approaches such as simulation modeling, discrete events simulation, or Bayesian networks have only limited ability to exploit continuous-time process models of such phenomena. Even worse, the accessibility and mechanistic insight into such gray- or black-box models are typically very low. To address these challenges, we suggest leveraging objectively [...]

If data is the new oil, artificial intelligence is the new vessel--and given enough data, AI can take us light years ahead. In a 2020 analysis of businesses leveraging big data by the International Data Group (IDG), it was revealed that small and medium-sized enterprises manage about 50 terabytes of data--a figure that was expected to grow by a margin of 50% over the coming year. This becomes an intriguing figure as small-medium enterprises accounted for 99.9% of the business population in the UK at the start of 2021. One may be led to believe that an estimated 5.5 million SMEs generating large amounts of data would mean that the said data is being actively employed in analytics--however, it may be a faulty presumption. The truth is that infrastructural challenges unique to SMEs oft act as barriers to the effective utilization of data analytics.

Your Home Is Your Castle; Don't Forget The Moat In this week's lead quote, a16z's Casado and Martin claim diminishing returns to scale for more data. This is a bold proposition because it undermines a key argument for many data programs: that more data creates a defensive moat to protect the enterprise. Certainly, all organizations in competitive markets want to create defensive moats, and data can help. But it has to be done the right way. The right way to build a viable data-based defensive moat is specific data, not more data.

Only 10% of companies obtain significant financial benefits from artificial intelligence technologies. Our research shows that these companies intentionally change processes, broadly and deeply, to facilitate organizational learning with AI. Better organizational learning enables them to act precisely when sensing opportunity and to adapt quickly when conditions change. Their strategic focus is organizational learning, not just machine learning. Organizational learning with AI is demanding.

Training employees how to locate and use data insights is a major Big Data bottleneck. Even if businesses have made sizable investments in data and analytics, every employee doesn't necessarily understand how to properly use that data. Learning enough to take advantage of those investments requires time and effort for each employee. While deep reservoirs of data may exist in the organization, the flow through individual employees may be more of a trickle than a cascade. Artificial intelligence-based approaches may be able to help by enabling each employee everywhere to know what the organization overall knows somewhere.

Training employees how to locate and use data insights is a major Big Data bottleneck. Even if businesses have made sizable investments in data and analytics, every employee doesn't necessarily understand how to properly use that data. Learning enough to take advantage of those investments requires time and effort for each employee. While deep reservoirs of data may exist in the organization, the flow through individual employees may be more of a trickle than a cascade. Artificial intelligence-based approaches may be able to help by enabling each employee everywhere to know what the organization overall knows somewhere.

Artificial intelligence is a hot topic right now. Driven by a fear of losing out, companies in many industries have announced AI-focused initiatives. Unfortunately, most of these efforts will fail. They will fail not because AI is all hype, but because companies are approaching AI-driven innovation incorrectly. And this isn't the first time companies have made this kind of mistake.