- To help industrialize their Analytics capabilities, enterprises will look up to their partners.
- Harnessing the possibilities with data will be essential for Digital Transformation (DT).
¿What does it mean to be Data Native?
- Data Native is a company that bases its decision making on data collected from sources such as social media.
- They are “native” because they were born feeding off data in such a way, in a data environment.
Example of Data Native: Uber would´nt be what it is without its Data Analytics:
- It wouldn´t have a clue as to how its business functions.
- It’s not just that it’s based on a mobile app, but without its data its business would come to a halt instantly.
Non Data Native example: traditional banks:
- Even without its Data Analytics, their clients will continue paying mortgages though their bank accounts, and their savings would still be deposited there.
- They will survive en the medium and even the long run.
The future of data is linked to constantly improving:
- Data acquisition
- Actioning on the data. It will make a difference.
- Capabilities like Mobile Analytics and Embedded Analytics are being offered by BI platform vendors.
- This minimizes the heavy lifting for maintenance and reporting, traditionally reserved for the CIO.
- That, in turn, increases agility, recasting the possibilities of what you could do with BI and Analytics.
- With the help of technology, businesses can create robust processes which ensure long-term GDPR compliance.
- Organisations spend an enormous amount of time, money and resources to achieve GDPR compliance.
- With a Data Layer architecture, enterprises can deploy part of the data layer on premise and then move the data in and out of any cloud at any time, with no downtime or re-writing of their applications.
- A well-constructed Data Layer can act as both a common dictionary to support your digital marketing applications and a unifying road map for communicating with your customers.
- Without a model to think about your customer interaction data, you cannot unite your applications around common definitions.
- The Data Layer represents the promise of omnichannel marketing that many digital marketers strive to achieve.
- IoT (Internet of Things) and BDA (Big Data Analytics) competencies lead to important value in business processes, if backed by high data quality, delivering competitive advantage.
- Complex business processes, fed with bad data, decreases BDA and IoT capabilities.
- With a proper data quality framework, BDA and IoT diffusion can be raised.
- Eliminating a single root cause can prevent thousands of future errors, save millions, and make things better for all involved.
- While some data quality issues are unfathomably complex, many yield quickly and produce outsize gains.
Social media diversity and BDA will have a positive interaction effect on market performance, more salient for SMEs than for Large Firms.
- Example: Telecom companies need to undergo a fundamental digital marketing and sales transformation.
- If they don’t, not only will they pass up an opportunity to increase market share, they risk losing their existing market share.
- With GDPR introduced in 2018, many businesses quickly advanced their Data Governance programs.
- But this didn’t translate into a better understanding of data and new analytical insights.
- On the positive side: different lines of the same business were able to foster open communication.
- In 2019, businesses will introduce governance to analytical models:
- So they can aggregate the metadata around their models…
- To ensure all teams have complete understanding of their data to leverage it for insights.
- Only companies that adjust constantly their Non-Invasive Data Governance Operating Model of Roles & Responsibilities will succeed in the coming years.
- The Chief Data Officer (CDO) will be increasingly important in the Data Governance structure.
- Without a clear strategic vision on what to do with hundreds of terabytes of data, companies can quickly get bogged down in the details. Read more
- Is the rise of CDOs a permanent fix or a temporary one?
- For a data strategy to work appointment of CDOs can only ever be a temporary fix.
- CDOs are there to augment and enhance an existing operating model with data, championing that and making it happen.
- There comes a time when that job is done and the organisation is more or less transformed.
- When an organization is naturally thinking this way, the CDO as catalyst is no longer necessary. Read more
- The amount, variety and scope of business data available will continue to grow exponentially.
- Organizations will have to work relentlessly to identify and reduce islands of bad data by moving rapidly to adopt Agile data processes so they can move quicker and be more responsive to internal and external data needs.