How to Leverage Internal and External Data to Improve Your Company’s Organizational Health, Employment Brand and Revenues

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How to leverage internal and external data to improve your company’s organizational health, employment brand and revenuesAs evidence of the value of data-driven decision-making mounts [1], clients request guidance to map out a strategy to better understand, manage, and leverage the data available to them. Beyond the technical questions, we often find the real challenge is to help companies develop a data culture and a data vision that moves beyond traditional data resources and metrics. To that end, we’ve prepared this White Paper focused on data sources, metrics, and algorithms. We hope this White Paper will broaden your data horizons and help you imagine new questions you might pose, new insight you might gain, and new stories you might tell, if you had easy access to these kinds of information. As an illustrative example, we describe the data sources, metrics, and algorithms that could be used in an internal “Organizational Health Check” service. This concept is at the core of Plazabridge Group’s GameDay Decisions analytics platform generating the Automated Health Index™.

Data SourcesAll organizations collect data intended to monitor and report their success. All employees have contributed to and engaged with these data at some point...whether filling out application forms and surveys, or entering and

Our imagination is stretched to the utmost, not, as in fiction, to imagine things which are not really there, but

just to comprehend those things which are there.- Richard Feynman, The Character of Physical Law (1965)

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retrieving data relevant to their job. What has changed in recent years is the ability to integrate data across all aspects of the organization, gather data and insights from beyond the organization boundaries, and automate many of the data retrieval and summary processes [1]. The outcome is to generate the space and time for decision-makers to integrate data with judgement and intuition. This allows them to pose more interesting questions, gain deeper insights, test assumptions to reduce risk, anticipate emerging opportunities, and tell more compelling stories.

Below we outline some examples of internal, external, and supplemental data resources. As you read through the lists, we challenge you: (1) to think beyond the immediate data you collect - think also of the meta-data (data about data, such as when and where it was collected), (2) to imagine connections or associations among the data that you would wish to explore, if the data were immediately accessible and easily visualized, and (3) to explore how perceptions of data might change if you had not just the data, but also knowledge of the social context and dispersal patterns of that data.

Internal data source examples:

Traditional internal data (HR, marketing, etc.) provide information on employee turnover, sales success, and profit margins. Non-traditional internal data resources provide insight into the social structure and dynamics within the organization, sentiment expressed by different groups within various informal media, and tone of leadership expressed through formal media.

• HR data (retention, performance, promotion, raises, benefits, leave, time-to-hire, demographics, addresses, employee surveys)

• Marketing/Accounting data (sales/services dates, values, and responsible individual/team, customer surveys)

• Memos (date of, sender, recipients, format/access, text content)

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• Policy documents (date of, format/access, text content)

• Employee performance reports (date of, reviewer, reviewee, scores, query language, text content)

• State of business reports (date of, department, author, audience, format/access, text content)

• Internal email/message systems (date of, sender, recipient, internal social network structure, text content)

• Databases (date of access, data pulled, data updated)

• Website (date of access, pages visited, information downloaded, text content)

• IT logs (log-ins, activity, security, usage patterns)

External data source example:

External data can serve multiple purposes [2]. Social media data (e.g., Facebook, Twitter, Instagram) provide insight into behavior, beliefs, motivations, and values of “people like ours” generally or by demographic group, or specifically for your employees. Professional media data (e.g., Glassdoor, LinkedIn, ResearchGate) provide an alternate and complementary perspective on individuals’ beliefs, motivations, values as well as the reach of organization’s workforce expertise and reputation. News media or weather data provide insight to events, trends, patterns that could externally influence populations within an organization.

• Social media data (social network structure, text content, preferences, propensities, dates, locations)

• Professional media data (social network structure, text content, dates, locations)

• Blog data (readership network structure, likes, shares, downloads, text content)

Gain insight into the social structure and

dynamics within the organization

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• News media data (unanticipated and anticipated high impact event dates, locations, likes, shares, text content)

• Weather data (10 day forecasts, extreme weather events, pollen forecast, flu forecast)

Supplemental data collection:

In some cases, supplemental data collection serves to fill gaps or check assumptions underlying models and analyses. Alternatively, they provide novel insights and can provide the basis for new questions or hypotheses, especially as related to organizational culture and collective behavior.

• Narrative data - Anecdotal narrative fragments scored by the individuals who provide the narrative provide an alternative perspective (or check) of sentiment analysis. The insights are gained where individuals self-score their sentiments differently than algorithms. Clustering of anecdotal experience also provides contrast to clustering performed on demographic or behavioral data. Finally, narrative can identify biases and false hypotheses that may have been built into your analytics and business strategy [3] .

• Sensor data - With the Internet of Things [4], sensor data can provide valuable insight into use of equipment and space, as well as personal activity, health, and safety.

External data includes social,

professional, and news or

weather data

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Human Resources RecordsMarketing/Accounting DataInternal CommunicationCompany Policy DocumentEmployee Performance EvaluationsState of Business ReportsFacilities Management DataInformation Access RecordsInternal WebsitesIT LogsSecurity Video AnalysisOn-line video activity (e.g. Skype)Operation Research ReportsCustomer Service Activity

Organization Charts with Roles & ResponsibilitiesNarrative DataIoT/Sensor dataInternal Social Media applications (e.g. YAMMER)Social Media ActivityProfessional Organization ActivityBlogging and EngagementNews Outlet ReadershipEnvironmental Data; Weather, etc.Community InvolvementPolitical Activity (e.g., local union)Financial Outlet Information(from Gameday Decisions)

Data Sources (partial list)

Plazabridge Group's GameDay Decisions Platform product suite of Artificial Intelligence & Machine Learning Applications includes two applications: a market development application leveraging machine learning to accelerate knowledge of market conditions and support strategic decisions; and a second application under development to measure the health of a company’s organization through non-intrusive data analysis. In addition to helping clients innovate by developing a data analytics culture, Plazabridge Group brings integration expertise to realize the value of innovating through analytics.

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GameDay Decision Platform is a fully automated, autonomous application collecting of data to drive new insights and revenue. The GameDay Organization Health Index™ collects internal and external data to measure various dimensions of an organization. With the Innovation Dimension dashboard, companies can monitor and influence their organization’s ability to innovate and openly collaborate. By measuring data indexes around key indicators of innovation, the tool builds a use case. Iterative data measurement reduces statistical errors and provides valid insights.

The collected data is supplemented with analysis of contextual data from YAMMER, emails, forums and collaborative online discussions to further uncover insight into how the organization’s innovation culture is evolving.

Storytelling, open narratives with sentient analysis.

D. Snowden, “The New Simplicity: Context, Narrative and Content,”JournalofKnowledgeManagement5,No.10,11–15 (July–August 2002)

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Data Metrics

The data sources described in the previous section have multiple dimensions. Isolating the data into distinct data silos ignores potential relationships among variables. Yet, neither is it adequate to simply merge the data and seek correlations among variables. There are temporal, spatial, contextual elements that encourage the exploration of dynamic, emergent patterns. These patterns could be characteristics of or relationships among entities (individuals, groups, programs, departments, etc.). Characterizing these patterns (or triggers of pattern shifts) in relation to organizational health facilitates early detection of potential opportunities and problems. In the case of an Organizational Health Check, the goal is to strengthen positive patterns and encourage positive change while dampening negative patterns and change.

• Network metrics: number of nodes (individuals), connectedness of individuals and groups, information and opinion flows (direction, volume, speed), influencers vs influenced, geographic (or departmental) distribution

• Cluster metrics: dominant group characteristics or behavior, number of members within group, dissimilarity between groups, similarity within group, correlation (causal or simply probability of joint occurrence), outliers

• Change metrics: transitions, trigger points, thresholds, trajectories, disruption

• Sentiment metrics: beliefs, motivations, values, mood, emotion, judgment

The response of interest for our example, organizational health, would be defined by a set of metrics based on current theories of successful

Characterize patterns to facilitate potential

opportunities and problems

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business practices. For example, if innovation is a key characteristic of organizational health, then the objective is to identify factors and patterns that trigger, maintain, or accelerate innovation (or the reverse). This implies that innovation has been defined is such a way that it can be clearly measured as present versus absent or slow versus fast.

Good organizational health metrics accurately describe (past), indicate (present), and sometimes anticipate (future) the status of an organization’s performance. Traditionally, distinct sets of organizational health metrics independently focused separately on various aspects of organizational health, such as employee and leadership culture, financial success, and strategic vision. However, these diverse aspects are closely linked and inter-connected; changes in one aspect generally precipitate change in other aspects of organizational health.

Ignoring these linkages limits the power of organizational health checks to the past and present and forces companies to be dependent on assumptions that patterns in the past will effectively prepare them for the future. A quick review of current business literature reveals the danger of such assumptions. Current theory emphasizes complexity, emergence, and disruption of patterns. Maintaining organizational health requires not just knowledge of the past and present, but also understanding of the structure and dynamics that enable the company to innovate, adapt, and maintain organizational health.

Organizations that leverage the technological innovations of Big Data and Artificial Intelligence gain direct insight into the complexity, emergence, and disruption that drive and define organizational health.

Algorithms

Algorithms are problem-solving tools. Multiple algorithms have been developed to handle the large, complex data sets and metrics mentioned

Some algorithms categorize

cause and effectrelationships

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previously. When working with Big Data, analysts use algorithms for diverse tasks. Some algorithms calculate derived variables (e.g., a rate of change, diversity classes), some characterize the nature of things (e.g., clustering, grouping), and some define cause and effect relationships [1]. A single analysis might involve all three kinds of tasks. For example, an organization interested in exploring how diversity influences organizational health, could first calculate a diversity index and a health index, then classify time periods or departments according to the health index, and then test if changes in the diversity index positively correlated with changes in the health index.

The choice of a specific algorithm from a set that perform similar functions depends on the data (quality, quantity, relevance, and accessibility) and computing resources. Given suitable data (and the definition of suitable is very broad for Big Data and AI applications), some common procedures and applications include:

• Apply external data to strip out or control for background (external) factors influencing internal behavior and sentiment. More clearly see the patterns within the organization’s direct sphere of influence and/or leverage knowledge of external factors to inform hiring, marketing, or management strategy.

• Identify external and internal factors that commonly correlate with positive metrics (diagnostic) or precede change (anticipatory) to inform organizational strategy.

• Explore differences among demographic groups, departments, or levels within organization to identify patterns of diversity (social and cognitive), coherence, and divergence.

• Enable continuous evolution of correlative relationships (and health assessments) through incremental learning algorithms. Expectations need not be set upon static assumptions of values, beliefs, and motivations or constant values weighting the importance of each data resource.

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Machine learning algorithms are those most commonly associated with Big Data analysis. Jason Brownlee [5] created a mindmap of machine-learning algorithms and offers two perspectives on how someone might think about types of algorithms. First, he distinguishes algorithms by learning style: supervised, semi-supervised, unsupervised.

Supervision, in this context, is when the data analyst provides a training dataset to constrain or guide the algorithm’s procedure. For example, if organizational data can be sorted a priori into unhealthy versus healthy periods or practices, algorithms can use distinguishing characteristics of the two groups to classify new data as it arrives. Alternatively, if training data are unavailable or the analyst simply wants to avoid enforcing assumptions associated with a priori labeling, then an unclassified algorithm will seek patterns based solely on the data structure. Many algorithms have supervised or unsupervised variants - so this classification of algorithm types relates to your assumptions about your data and your knowledge of how the system works.

Brownlee’s second classification system organizes (and illustrates!) common algorithms into eleven types based on the procedure used by the algorithm to answer a question [5]. Of course, many algorithms defy easy classification because data analysts find ways to mix and match methods as they seek better accuracy and higher precision.

Re-imagine Your Data

Clients often find the prospect of transitioning to data-driven (or, as I prefer, data-informed) operations. Indeed, there can seem to be an overwhelming volume of data resources and diversity of possible algorithms. However, qualified data managers and analysts will be responsible for the methodological details of integrating the data and selecting algorithms.

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With the above material, our goal has been to broaden your knowledge of what data you have and what new questions you might pose with these data.

The questions you can pose to your data relate not just to the measured values - but also their sources, their rates of change, their dispersal as information, and their impacts within and beyond the organization. Furthermore, your data are not constrained to when individuals intersect with your organization, but extend beyond your direct interactions in time and space.

A common side-effect of data integration is the automation of routine tasks, such as periodic reporting. At each level of the organization, individuals can spend more time investigating the implications of the data or innovating applications of the data, rather than assembling the data.

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[1] Anderson C. 2015. Creating a Data-Driven Organization: Practical Advice form the Trenches. O’Reilly, Sebastopol, CA. http://shop.oreilly.com/product/0636920035848.do

[2] Machine Learning from Streaming Data. March 12, 2013. https://blog.bigml.com/2013/03/12/machine-learning-from-streaming-data-two-problems-two-solutions-two-concerns-and-two-lessons/

[3] Nudging People Towards Latrines. March 11, 2016. http://cognitive-edge.com/blog/nudging-people-towards-latrines/

[4] Internet of Things: An Executives Organizational Roadmap. January 6, 2016. http://businessintelligence.com/bi-insights/internet-of-things-an-executives-organizational-roadmap/

[5] A Tour of Machine Learning Algorithms. November 23, 2013. machinglearningmaster.com. http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

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Author Page:

Dr. C. Ashton Drew has over 20 years’ experience focused on the challenges of decision-making and planning within complicated and complex systems. Trained in ecological sciences and natural resource management, her work combines quantitative and qualitative data analytics from both natural and social sciences to address emergent challenges. As a collaborator with PlazaBridge Group, she assists organizations to evolve the data culture and awareness necessary to transition to data-driven, anticipatory practice.

EDUCATION

Beloit College, WI, Environmental Biology, BA 1995Dalhousie University, NS, Marine Management, MMM 1996North Carolina State University, NC, Marine Biology, PhD 2006North Carolina State University, NC, Landscape Ecology, Post-doc 2006-11

SELECTED PUBLICATIONS

– Drew CA, Collazo JA (2014) Bayesian Networks as a Framework to Step-down and Support Strategic Habitat Conservation of Data-poor Species: A case study with King Rail (Rallus elegans) in Eastern North Carolina and Southeastern Virginia. Prepared for the USFWS Region 4, Raleigh Field Office, NC by the USGS – North Carolina Cooperative Fish and Wildlife Research Unit, NCSU

– Drescher M, Perera AH, Johnson CJ, Buse LJ, Drew CA, and Burgman MA (2013) Toward rigorous use of expert knowledge in ecological research. Ecosphere 4(7): 83

– Costanza, JK, Abt R, Drew CA, Gonzales R, Collazo JC (2013) Modeling Impacts of Biomass Production on Landscapes and Wildlife. Final Report to the Biofuels Center of NC

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– Drew CA, Alexander-Vaughn LB, Collazo JA, McKerrow A, Anderson J (2013) Developing an Outcome-based Biodiversity Metric in Support of the Field to Market Project. Technical Bulletin 334, NC Agricultural Research Service, College of Agriculture and Life Sciences, NCSU

– Hightower, JE, Harris JE, Raabe JK, Brownell P, Drew CA (2012) Habitat suitability models for American shad in southeastern rivers. Journal of Fish and Wildlife Management 3(2):184-198

– Drew CA, Alexander-Vaughn LB, Collazo JA, Reid JP, Slone DH (2012) Science Summary in Support of Manatee Protection Area (MPA) Design in Puerto Rico. Technical Bulletin 330, North Carolina Agricultural Research Service, College of Agriculture and Life Sciences, NCSU.

– Perera AH, Drew CA, Johnson CJ (eds) (2011) Expert Knowledge and its Application in Landscape Ecology. Springer, New York

Pijanowski B, Iverson L, Rhemtulla J, Wimberly M, Drew CA, Bartsch A, Bulley H, Peng J (2010) Addressing the interplay between poverty and landscapes: a grand challenge topic for landscape ecologists. Landscape Ecology 25:5-16

– Drew CA, Wiersma Y, Huettmann FH (eds) (2010) Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications. Springer, New York

– Hess GR, Rubino MJ, Koch FH, Eschelbach KA, Drew CA, Favreau JM (2006) Comparing the potential effectiveness of conservation planning approaches in central North Carolina, USA. Biological Conservation 128: 358-368

– Favreau JM, Drew CA, Hess GR, Eschelbach KA, Rubino MJ, Koch FH (2006) Recommendations for assessing the effectiveness of surrogate species approaches. Biodiversity and Conservation 15: 3949-3969

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COURSE DEVELOPMENT AND INSTRUCTION

Interactive Data Visualization and Application Development with R (2016)Climate Change and Conservation Planning (2009)Hierarchical Species-Habitat Analysis and Conservation (2007 & 2008)Natural Resource Measurements (2003)Landscape Ecology (2002)

CERTIFICATIONS AND TRAININGHadoop, MapR, and AWS Elastic MapReduce (various: 2016)R Programming and Data Analysis (various: 2014-2017)Cynefin & Sense-Making (2015), SenseMaker Environments (2015), and Signification Design (2015)Analysis of Community Data with PCOrd and R (2014)Bayesian Network Analysis with Netica (2013)Introduction to Guidos (2013)Introduction to R Programming (2012)Elicitator & Probability Elicitation through Scenario Analysis (2011)Modeling Patterns and Dynamics of Species Occurrence Workshop (2007)Model Based Inference in the Life Sciences Workshop (2007)ArcGIS and Spatial Analyst (2006); Introduction to Arc/Info (1999)

For more information:

Marie-Claire@plazabridgegroup.comRichard@plazabridgegroup.comAshton@plazabridgegroup.com More info about Plazabridge Group:

www.plazabridgegroup.com