The Analytics Skills of the Digital Innovator
In 2017 and beyond, innovation with digital technologies is no longer a strategic option: it has become a necessity. Certain companies like Facebook, Alphabet, Apple, Amazon and IBM have become so adept at analytics that we refer to them as digital titans. In fact, these companies present a threat to every business with their superior information processing and analytics capabilities.
IDC predicts that the digital transformation (DX) economy will drive 60% of IT spending by 2020.1 Laying over the solid foundation of mobile computing, social networking, cloud computing, analytics, and IoT technologies, DX will drive competitiveness and profit to new heights.
Companies increasingly rely on their capacity to innovate with digital technologies. According to Peter Sondergaard, senior vice president at Gartner and global head of Research, leading CEOs expect their digital revenue will increase by more than 80 percent by 2020.2 Yet their goals may be hindered if they do not get right the skills and competencies required to master the technologies supporting digital innovation.
“Quality data is critical to effective digital innovation, and the digital innovator should aim to become an adept at analytics.”
Digital startups face additional challenges. They must acquire competencies to effectively manage the foundational technologies at the same time that they must deploy these technologies to lead digital innovation and transformation efforts.
Quality data is critical to effective digital innovation. The success of digital businesses depends on market feedback, data streams (e.g., APIs’ data inflows and outflows), experiments, and the discovery of business opportunities. Among the competencies companies need in the DX economy, analytics is chief. The digital innovator should aim to become adept at analytics.
These are the top 6 analytics skills that the digital innovator should cultivate:
- Data management. Those who spend their days doing analytics know most of their efforts go into obtaining, cleaning, integrating, and managing data. For the analytics-master innovator, data management skills involve much more than manipulating columns in Excel. The digital innovator should be able to tap into streams of data both private and public. This requires them master the use of APIs behind the firewall and outside, administer customer surveys, and incorporate them into the organization’s data flows. The digital innovator should know the principles of API data exchanges; understand data storage, both for structured and unstructured data (i.e., relational and NoSQL databases); and command query languages (e.g., SQL, the most widely used analytics tool,3 used to query and edit data in relational databases). In addition, the digital innovator should know how to clean and transform data for analysis, a tedious process that can be standardized by creating workflows in R or Python.
- Statistical programming. Despite the improvement and expansion of automated analytics solutions, a digital innovator should learn statistical programming. For one, automation leads to standardization and loss of flexibility, both enemies of the innovative development of algorithms and processes. The digital innovator should rely on statistical programming environments affording agility, modularity, scalability, and composability. Data scientists overwhelmingly prefer R and Python,3,4 both featuring large ecosystems and ever-expanding functionality through packages/libraries contributed by large communities of developers.
- Statistics application acumen. Automated analytics tools facilitate the preparation of data, design of visualizations, and building and interpretation of statistical models. Unfortunately, they also tend to release users from the responsibility of checking and accounting for the statistical assumptions of the data (e.g., validity, sampling, generalizability) and the assumptions of the models built. When the digital innovator relies on small experiments and continuous market feedback to perfect the innovation – widely touted by the Lean Startup methodology – statistical acumen is important to derive the right conclusions from data.
- Data visualization. Organizations struggle to attract and retain business users with analytics skills.5 Despite their efforts to create a culture of analytics, companies find that large majority of their employees, including executives, lack the statistical expertise to interpret and strategize on statistical models. The digital innovator should have the ability to create data visualizations that clearly convey findings in support of strategic digital actions. In this space, solutions automating the creation of charts and the writing of compelling narrative (i.e., Natural Language Generation software such as Narrative Science for Tableau) offer the digital innovator easy-to-learn alternatives to hands-on data visualization tools (e.g., gpplot2 R package).
- Communication. The digital innovator should be an effective communicator of analytics findings, if she is to gain broad organizational support to invest organizational resources in inherently risky innovations. This should include excellent oral and writing communication skills, and the ability to teach analytics concepts to executives and business partners.
- Culture. Often ignored, prevailing culture and its receptivity to evidence based management will dictate adoption of analytics. If this culture is absent, company leadership must spend time and energy into changing the culture first. At Google, when Marisa Meyer made product decisions, she would often stop presentation if arguments were not supported by data. This sent a clear message to the rest of organization – In God we Trust, the rest of you bring data!
This is the second post of a series of articles on analytics and digital innovation. We invite you to leave your comments and read the first post: “Analytics is the Key to Digital Innovation.”
1 IDC (2015). IDC Predicts the Emergence of “the DX Economy” in a Critical Period of Widespread Digital Transformation and Massive Scale Up of 3rd Platform Technologies in Every Industry. Press Release. Available at: https://www.idc.com/getdoc.jsp?containerId=prUS40552015
2 Gartner (2015). Gartner Says It’s Not Just About Big Data; It’s What You Do With It: Welcome to the Algorithmic Economy. Press Release. Available at: http://www.gartner.com/newsroom/id/3142917
3 King, J. and Magoulas, R. (2016). 2016 Data Science Salary Survey – Tools, Trends, What Pays (and What Doesn’t) for Data Professionals. O’Reilly Media. Available at: http://www.oreilly.com/data/free/files/2016-data-science-salary-survey.pdf
4 KDNuggets (2016). SAS vs R vs Python: Which Tool Do Analytics Pros Prefer? Blog post. Available at: http://www.kdnuggets.com/2016/07/burtchworks-sas-r-python-analytics-pros-prefer.html
5 McKinsey (2016). The need to lead in data and analytics. Online report. Available at: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-need-to-lead-in-data-and-analytics