Digital Intelligence Management The Transformation in Market Management

Theoretical Baseline and Best Practices

Gunter Nowy Berlin, 29 September 2017

Hochschule für Wirtschaft und Recht

Agenda

2

1. Digitisation - Acceleration or new concept?

2. The Great Transformation - Shiftings in markets and corporate management

3. Challenges in corporate management - Closing analytical gaps

4. Case market management - Transforming big data to smart data

5. Digital Intelligence Management (DIM) - A new paradigm as a strategic factor

6. DIM constraints - Embeddedness of markets

7. Best Practice - Social Value of Soccer Players

8. Bottom Line - Conclusions for corporate management

Digitisation

3

‣ The binary number system - A mathematical concept revolutionaries the world

‣ „Enlightenment of labour processes“ - Electrical impacts in the economic revolution

‣ New theories on information - Between natural science and humanities

‣ Cyberspace - Crossing borders in economies and societies

The Great Transformation

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‣ Industrial Dimension - e.g.: Music Industry, Video Industry, Print Industry

‣ Organisational Dimension - e.g.: Mobile Gadgets, Social Networks, Remote Working Spaces

‣ Communicational Dimension - e.g. Blogging, Posting, Social Media

‣ Strategic Dimension - e.g.: Intelligence Techniques, Big Data, Data- and Text Mining

‣ Economic Dimension - e.g.: E-Payments, E-Trading, E-Commerce

Challenges in corporate management

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‣ Confidence in decision making processes

‣ Exploration of alternative reliable market information sources

‣ Identification of new opportunities

‣ Development of new business ideas & models

‣ Integration of social relations in economic models

‣ Usage of new analytical techniques and customised tools

The case of Market Management - The 3Gs research paradigm

Google search Gut feeling Guys with MBAs

Relevance of information? Unclear

Sources? Unclear

Processing? Unclear

Information quality? Random

Methods & transparency? un-systematic

Structure of information? In-coherent

„Taste“ & quantitative limited

„In numbers we trust!“

„Stuck in the box“

Algorithm? Unclear Causality? Poor Unreliable

Analytical approach & capacities?

Reflection of methods?

Horizon?

Quality of sources?

6

Individualized Precise Automatization

Profile-based

Finest selection of sources

Analytical standards

contextualized, accurate, significant

Transparent methods

lean & clean (un-limited & scientific)

Systematized analysis, retrie-val standards

„Hubblebilities“

Defined by your profile

Proven and reconstructable

Reliable: triangulation & verified Data

IPA-Recipe - An intelligence approach

Complete puzzle

Relevance of information?

Sources?

Processing?

Algorithm?

Information quality?

Methods & transparency?

Structure of information?

Causality?

Analytical approach & Capacities?

Reflection of methods?

Horizon?

Quality of sources?

7

Intelligence & Digitization

Data volume

Data velocity

Data variety

Information collection

Information processing

Information analysis

Digital tools

Digital aggregation

Digital analytics

Market intelligence

Digital revolution

Digital intelligence + =

8

Digital Intelligence Management (DIM) - In a nutshell

‣ Information sourcing & gathering

‣ Information streamlining & compressing

‣ Information assessing & judging

‣ Smart data

‣ Clean data

‣ Structured knowledge

‣ Transparent network structure

‣ Big data

‣ Data smog

‣ Unstructured information

‣ Intransparent network structure

Intelligence approach

Digital paradigm

Management of individual solutions

99

Average increase of global data volume Source: Own Depiction

Bus

ines

s va

lue

Database research • Meta data analytics • Office management • Data mining • Online services - www

Open source intelligence • Internet research • Media analysis • Web analytics • Public data

1990 - 2000 2001 - 2010 2011 - 2020 2021 - 2030

Market intelligence • Big data • Social media • Mobile web • Cloud services • Content analytics

Digital intelligence • Profile based data • Text mining & corpus

linguistic • Roboter journalism • Smart data management • Data driven leadership

Potential added value Paradigm shift

Historical abstract - Evolution

10

DIM constraints - embeddedness of markets

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‣ Categories / characteristics of market procedures ▪ economic values (e.g. prices for goods or services) ▪ economic transactions (e.g. payments via credit cards)▪ social actions (e.g. interactions like talking & posting)▪ cultural values (e.g. existence of brands or group identity)

‣ Markets are temporary constructs - their consistence is fluid, complex and they are composed by informations (edges) and actors (nodes)

‣ Markets can be visualised with KPI’s and via measurement tools

‣ Concepts like social learning do exist in markets and networks; they aim on benefits from the wisdom of the crowd

‣ Market information can be scraped from tracking (e.g. machines, IOT) or publishing (web, social media) repositories

Key essentials for DIM

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‣ Reach your target groups and observe them - also in B2B environments as well as in B2C markets

‣ Expand your information sources - from interpretation of data sets to distant reading

‣ Find the wise guys (or organisations) and identify successful business cluster

‣ learn from your competitors, customers and suppliers

‣ Build sociocopes and use living labs as realtime tools for market monitoring

‣ Define your business and demands - from idea development to controlling

‣ Find your observation fit - between isolation and echo chambers

Best Practice - Social Value in the Soccer Industry

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Digital Social Value Score

PERFORMANCE Social Media Performance

Evaluation of Channel Performance • Follower-/Fans • Post / Shares• Interactions between

Follower/Fans (e.g.. „Re-Tweets“, „Likes“, „Reactions)

• Growth Rates• Weighting factor

Ranking(s)

02

Sample Group

TOP 100 Professional Soccer Player of the German Bundesliga (based on the estimated marked value on www.transfermarkt.de)

Research of Key ChannelsFacebook, Instagram, Twitter und YouTube

Definition & Monitoring of Key Performance Indicators

DATA SETS & MONITORINGTarget Group & Services

01

VALUATIONEstimated Social Value

Account Assessment (in €) & Key-Metrics• Follower-/Fan • CPM (TKP) per channel• Account operation period

(based on players age)• Factor of interactions• Growth Rates• Market value of a player• Risk factor

03

14

Key Results - Social Media & Bundesliga (click for deeper informations)

Best Practice - Social Value in the Soccer Industry

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Player Mentions - Measurement of social actions

1

Best Practice - Social Value in the Soccer Industry

16

Social Value - Stars, Kings & Underdogs

Digital Social

Mar

ket

= Follower / Fans in allen Channels

DOUBLE VALUE StarsHigh Market Value and

high Digital Social Value

1

UnderdogsLow Market Value & Low Digital

Social Value

SOCIAL VALUE Question Marks

High Market Value but Social Value is below

average

SOCIAL VALUE KingsHigh Social Value but below

average market value

Best Practice - Social Value in the Soccer Industry

James Rodríguez

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TOP 3 Measurements: From Social Value to Economic Values

159.526.480€ 50.738.784€ 45.880.787€

Mario Götze Robert Lewandowski

Total Value

21.000.000 - 0

66.000.000 - 0

74.000.000 - 0

64.000 - 0

Best Practice - Social Value in the Soccer Industry

Bottom Line for the corporate management

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‣ Leave traditional analytical silos like google search and excel (they might be limited for some approaches)

‣ Start Prototyping with Big Data Tools and Techniques

‣ View on data, generate knowledge and use it in different ways

‣ Start abduction with deeper analytics to develop unique cases for the future

Literature (extract)

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Adams, N.B., 2004. Digital Intelligence Fostered by Technology. The Journal of Technology Studies, 30(2).

Belsky, G., Why Text Mining May Be The Next Big Thing. Time. Available at: http://business.time.com/2012/03/20/why-text-mining-may-be-the-next-big-thing/ [Accessed August 10, 2015].

Friedman, U., 2012. Big Data: A Short History - How we arrived at a term to describe the potential and peril of today's data deluge. Foreign Policy. Available at: https://foreignpolicy.com/2012/10/08/big-data-a-short-history/ [Accessed February 5, 2016].

McAfee, A. & Brynjolfsson, E., 2012. Big data: the management revolution. Harvard Business Review, 90(10), pp.60–6– 68– 128.

Mithas, S., 2012. Digital Intelligence: What Every Smart Manager Must Have for Success in an Information Age, Finerplanet.

Moretti, F., 2013. Distant Reading, Verso.

Pentland, A., 2014. Social Physics - How Good Ideas Spread-The Lessons from a New Science, The Penguin Press.

Westerman, G., Bonnet, D. & McAfee, A., 2014. Leading Digital, Harvard Business Press.

Author & Contact

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almagenic UG Geschäftsführer: Gunter Nowy Eichendorffstrasse 34 50825 Köln Telefon: +49 (0) 221 16825870 E-Mail: gunter.nowy@almagenic.com