Chief data officer

Data initiatives fail to achieve business results as a result of an interrupted chain of command between technology, business, data/business intelligence, and product teams. Spectra Global helps companies navigate data complexity in a cookie less privacy first future by connecting data sources, augmenting first party data collection and building platforms for marketing and monetization purposes  . You can think of a Chief Data Officer as a service.

Getting data strategy right is not a technical role, it’s not primarily a product role, on the other hand, it is not an analyst role, and it’ll not just be a compliance role  . It takes a consistent level of skill to map the data flow and use tools to extract it, the curiosity of an agile researcher to find the best insights from the data, and the soul of a guardian who would protect the data from abuse. Spectra believes that the high level of craftsmanship makes it difficult for organization to find CDOs.

More importantly, CDOs who do not have all 4 skills usually have a hard time doing their job and are not masters. Non-specialists downgrade to an analytics or technology issue, or do not have the confidence to interact with business users and partners.

Enter the World of Data Craft – and embrace Spectra’s Chief Data Officer as a service. We believe the CDO defines  company’s strategic approach to data and convert it into a valuable asset for strong business outcomes embedded into the frontend of the organization.

We provide 4 key services

6 V Assessment of the data

1. Assess the value = velocity, volume, variety, veracity, visibility and vulnerability of your consumer data in the face of privacy , cookie transition and business needs .
2. Veracity and variety determines the utilizable amount of data for business.
3. Velocity determines its applicability to various points in the customer journey.
4. Volume determines the platforms and scope of the data management needed.
5. Vulnerability can be external or internal and compliance to various regulations.
6. Visibility shows if the data is usable by the frontline

Build New Sources and Optimize Data Gathering process

1. Build new sources of first party data, and if needed re-imagine the customer experience .
2. Collect data about the user while opening the app in a privacy compliant manner – run various experiments  and gamification.
3. Evaluate third party and second party sources.
4. Optimize the data gathering process.
5. Create data taxonomy to make sure there is consistency across the organization

Evaluate the right data platforms for success

1. Use ready checklists for comparing data lakes, cloud platforms , customer data platforms to aggregate data from multiple channels and sources , segment them correctly and activate it across different channels.
2. Identify all the data sources along the customer lifecycle– web, mobile, apps, CRM, communication channels, your product itself— just to name a few and map each data source for the 4 Vs.
3. Create the right data architecture – data cloud vs CDP vs data lakes.
4. Evaluate which data is needed in real time and what is needed as batch.

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Harness data and drive business use cases

1. Harness the data and work with the various business teams to build use cases.
2. Work with operations teams to implement the company’s higher-level data strategy, delivering continuously improving data to achieve specific business goals.
3. Manage the entire data landscape, including related processes and consumption both domestically and internationally.
4. Help the company productize and are responsible for organizing data, protecting data, delivering business value, and creating data products.
5. Define your audiences and target them, using the data segments to build personalized experiences if necessary .
6. Work with data analytics teams to make sure more time is spent on data model buildouts rather than accessing and preparing data.

Monetize the data for partnerships and ecosystem impact

Build partnerships with the ecosystem or channels to monetize the data . In some cases , data discovery and partnerships leads to new business models and value unlocking. Many banks have done this by leveraging their customer knowledge to enter new areas. This can be a powerful source of growth and value.