How to make Digital Trasformation (DT) profitable
Must decisions for a company to boost Digital Transformation and make it profitable:
- Define the type of company culture that would embrace DT and contribute to the company’s growth.
- Define the customer experience you want to create through specifying your mission and culture.
- Define the types of employees you want in your company, so Human Resources, when hiring pays more attention to culture than skills.
- Enable your company to change quickly and easily by hiring Agile people, open to new technologies, and by being a model yourself.
- Provide a clear leadership for your team: the C-level’s role in DT is paramount.
- Key decisions for DT spin around Data, Automation and Technology
- wearables, Industry 4.0, IoT, IoE, social networks, structured data, semi-structured data, unstructured data, volatile data …
- Does your company know where to get the data from?
- Are they the right data?
- Do you suspect there is more useful data not being used? And that unhelpful data is being used?
- Even if we have the “correct” data –not more, not less–, without order and quality it won’t help make the right decisions.
- Having the wrong data is like having a hardware deposit with thousands of references stacked up like a mountain, with no order… they don’t provide value if they cannot be used properly.
- You know how to solve this problem: Data Management:
- Data quality
- Storage design
- Content design
- Data security
- Data warehousing
- Meta data
- Master data.
- there are too many to store, clean, process and analyze data…
- The trick is not to deploy all of them but to select those our company really needs, dismissing the rest.
- Often it’s enough (and cheaper) to choose well-known tools, not the fashionable ones. For example:
- Ad-hoc analysis: Impala, Presto…
- Machine Learning:
- Deep Learning:
- Sql Queries: Hive, Hadoop.
- once we have the right data, and we know how to use the tools to capture and store it, what kind of Analytics is the most appropriate?
- Descriptive Analytics: just describes what happened.
- Diagnostic Analytics: shows the root cause of what happened.
- Predictive Analytics: with the root cause of facts we can predict what will happen when the same causes/tendencies are repeated.
- Prescriptive Analytics: if we can predict what will happen, then we can recommend the best option (decision) to take advantage of such predictions.
- Automated robots implement this kind of processing algorithms.
we need scalable, flexible, elastic and secure environments.
The cloud is the solution, as it provides:
- Scalability: to have more processing capacity when you need it. The firm pays for the capacity really used (cloud-as-a-service).
- Flexibility: you can use the maximum capacity available, or only part of it, as needed.
- Elasticity: you can choose how much of your capacity to dedicate to each process. Example:
- 90% for real-time processing, when quick answers are needed.
- 10% for batch processing.
- Security: companies should rely on the cloud like they do on banks:
- We rely on banks to keep our money…
- We rely on cloud companies to keep our data.
- Continuity and security are their business values.
in addition to the advantages of the cloud (scalability, flexibility, elasticity and security), Edge Computing will reduce the volume of data being moved from IoT components to the cloud…
- So automated robots will process some of the data themselves.
- 5G will accelerate communication between the robot and the cloud.
- Startups are Agile data-driven natives…
- They don’t have cultural problems, such as resistance to change or low understanding of data as a business asset.
- This is the key to make them real competitors of long-established big companies.
to prosper in a world of a constant transformation:
- Learn constantly about new tools and give up obsolete ways of working.
- It’s not all about technology: develop your creative and communication skills.
is a good example of adjuvant technologies working together:
- Machine Learning
- Real-time data processing
- Edge Computing
- 5G in the near future
- Identify their business’s key initiatives.
- Understand the opportunities opened up by Industry 4.0
- Create a holistic architecture to support these technologies.
- Employ design thinking for better business alignment and technology adoption.
- Create useful, secure and relevant data to power business initiatives.
- Insert Analytic insights into operational iterations.
- Leverage IoT to optimize operational and product performance to augment customer experience and monetize the technology.
- To avoid machine-based measurement ambiguity, choose metrics that are:
- Easily manipulated.
- Relevant to your business processes.
to survive in the market in the long term, workers need to adapt by becoming the best AI coworkers. How?
- Learning to exploit existing tools:
- Not by investing in the latest-fashion AI tech, but in the most appropriate to your purpose.
- Integrating yourself in the process: what is the best way to manage my work collaborating (instead of fighting) with AI?
- Learning to exploit existing tools:
- Learning constantly to combine creativity + experience with AI tools.
firms can expand RPA to make processes more resilient and efficient:
- Resilient: because Machine Learning is applied to Automation to learn how to recover from errors (technical errors and human mistakes), keeping the process on without interruption.
- Efficient: as Automation reduces human intervention in repetitive activities.
- companies need to prepare for threats such as:
- Phishing scams.
- Fake news campaigns about the company