Value of data in digital transformation

Value of data in Digital Transformation

19.8.2016Tomi Bergman, CEO / PartnerTalent Base Oy

Topics

• How is data connected to Digital Transformation?

• Data as a key enabler for new innovations• What does it take to benefit from data?

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Fast facts

• 40 experienced professionals working in demanding, business critical digitalization projects

• Focus on solutions design – specialized in information management, CRM and digital services

• Founded in 2007

How is data connected to Digital Transformation?


Why is it important to focus on the most important data?

Focus on data which

a) has a meaning and

b)can be reused

Data is fuel for digitalization

• Data is fuel for any digital processes• Disruption is the name of the game – data is

used heavily in a different, creative manner • Digitalization makes data more and more

visible – and data quality issues, too

Familiar feeling?

How to understand data?

BIGDATA

TRANSACTIONDATA

MASTERDATA

REFERENCEDATAMETADATA

AMOUN

TOFDA

TA

SEMANTICSANDREUSE

STRUCTURED

UNSTRUCTURED

The biggest challenge currently is to combine unstructured data and metadata driven digital content (e.g. documents, videos, blogs)

with structured data in order to bring business value

Seamless processes and systems require good quality data

• An underlying common denominator is seamless data that runs across processes, across systems

• For this to happen, there needs to be a common understanding on key entities, such as "products" and "customers”, and tight rules and discipline in maintaining the common part of the data

• Supporting technologies and organizational capabilities need to be in place, and overall data architecture needs to be flexible

• Effective digital process change and customer’s expected quality of services relies on secure information and platforms where information privacy and security is well taken care of

Typical challenges for process digitalization

Question: What are the biggest challenges associated with your efforts to digitize processes?Source: Cognizant Center for the Future of Work

Data as a key enabler for new innovations


Innovation model

Solutions use data in order to fulfill processes

Well-functioning processes enable

solutions

Available and reliable data across the

organization from different channels

Advanced analytics as a tool for innovation

• Nowadays, advanced analytics and use of machine learning algorithms are key methods for developing data-driven innovations

• The goal is to understand and describe potentially massive amounts of structured and unstructured data, and derive valuable insights from them– E.g. new services/offers, cost cutting, risk reduction, automation

• Data scientists working together with business and product development starts to be de facto – however, finding data talents is not easy

• Innovation doesn’t always mean creating new products– Process innovations via digitalization and automation (e.g. robotics) can

yield significant business value, too

Case Castrén & Snellman: global business partner map

Improved core master data maintenance enabled data visualization and better communication of Castrén’s partners, and increased brand awareness as international strong player

Case Facebook: artificial intelligence

+

A virtual assistant powered by artificial intelligence as well as a band of Facebook employees, dubbed M trainers, who will make sure that every request is answered

M proposes relevant content, services and products based on users questions

Case DAQRI: Smart helmet for industry

Smart helmet is used to create augmented reality for the industrial worker, including visual instructions, real time alerts, and 3D mapping

E.g. combining product data with installed base data, content and real-time sensor data for user’s helmet

Case Uber and Airbnb: dynamic pricing

Both companies are using algorithms to build dynamic pricing based on regional supply and demand

What does it take to benefit from data?


How to boost innovations with data?

• Understand economics and the potential of data

• Define & organize core (master) data within the organization

• Acquire right competences (human + technical) to use and connect big data to corporate (master) data

• Consider using “Data-labs” with access to all data

Data-labs for trial-and-error• Many big companies (like British Petroleum & British Gas) have

established “data-labs” for boosting their product and offering development

• Idea is to deep dive into data to discover innovative solutions with the help of right people and competences

• Transferring ideas and techniques across industries –benchmark also “unusual / non-related industries”– What can you learn from other industries?

• Key is to access all the data (inside & outside), including customer needs analysis

Note! Data labs are not the only option for data quality improvements and innovations – analyze the data and potential improvement factors and start to take coordinated steps to improve the quality.

Summary

• The importance of data in digitalization is increasing all the time – data is the fuel for innovations, processes are the engine.

• Digitalizing processes and creating new innovative solutions makes data visible – and data quality issues, too.

• Invest first on putting core information (master data) in order, and then start to reuse and combine it with big data.

• Acquire right competences (technical + human) to build new innovations with data.

THANK YOU!

Tomi Bergman, Partner / [email protected]

Data & Analytics

Value of data in digital transformation