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Human Resources (HR) is one of the most important fields in any industry because its business is people—helping people maximize not only their own potential but also their companies’ potential. As a result, few areas in business have as much to gain from big data as HR does.

As a freelance technology writer, with a master’s in communication studies, I’ve been fascinated in writing on topics which specifically highlight ways in which big data and machine learning are affecting the human experience. Many of the pitches I receive highlight exciting, high-level ways that big data and machine learning will affect our lives. But to be frank, I’m far more interested in understanding how these things can, and will, affect us now.

Big data is sweeping the business world, revolutionizing everything from marketing and sales to product development. And HR is no exception. Big data has the ability to touch, and improve, every facet of a company. Here are some of the ways Big Data can be leveraged in HR right now to bring the field into the future.

* Increase Employee Retention Rates

Hiring is an expensive process. Recruiting and referrals alone cost an average of $4,000. And it’s not just the search for talent that’s expensive—the training and onboarding process is too. There can also be disruptive ripple effects throughout an organization as other workers get accustomed to the new hire and before things settle into a comfortable rhythm.

If a company goes through the trouble and expense of hiring someone, hopefully that someone will stick around. Increasing retention is all about ensuring employees are happy in their roles and feel taken care of. Big data can help with these goals.

Some companies, including giants such as Walmart, leverage internal data from surveys, performance reviews, pay history, and other areas to predict which employees are most likely to stay or leave. These predictions can then be used to target employees for raises, bonuses, or other perks that encourage them to stick around. More importantly, the information can also be used to avoid workplace patterns that perpetuate employee turnover.

* Eliminate Bad Hires

Hiring the wrong person is one of the costliest mistakes a business can make. In addition to the onboarding costs mentioned above, an employee that’s a poor fit for the company can lower everyone else’s productivity and morale and affect your business’s bottom line.

The US Department of Labor puts the cost of a bad hire at 30% of the employee’s salary. And many C-level executives feel that the impact on morale and productivity is even costlier. Prescreening candidates using big data analytics can help minimize the chances of hiring a poor fit. The data can also help companies attract stronger candidates by improving tone of voice, branding, and outreach strategies.

* Place Employees Where They Can Be Most Useful

Using data analysis to determine where employees will be most effective isn’t a new tactic. Google has been doing it for years with Project Oxygen, an initiative that uses data analysis to find and grow potential management candidates. Project Oxygen has been highly successful—in an antimanagement culture, Google convinced employees of the value of management using data from the program.

Helping people reach their full potential is one of the cornerstones of HR. Any tool that makes this process more reliable and efficient is of enormous value. And that’s exactly what HR departments have now in the form of big data.

* Maximize Productivity and Morale

Data can help HR teams maximize productivity by pinpointing what makes workers feel most rewarded. It’s been shown that happy employees are more productive, so anything that keeps morale and motivation high should be leveraged to its fullest potential.

Every company has an HR component to it and big data can be applied to almost any company, no matter its size. Ultimately, the goals of any HR team are to help employees get more done and minimize company losses. The insights that businesses gain from big data analysis can be uniquely leveraged by HR teams to drive these goals. The next step to integrating big data into your HR department is to make a strategic plan based on your company budget and needs. You can check out SAP’s big data offerings to see which works best for you. Ensure you have the correct processes in place to then implement the learnings gleaned from your big data analysis to reap the full rewards. http://bit.ly/2Fx4OdY #SAP #SAPCloud #AI

ICYMI: The problem this blockchain project is trying to solve is allowing a number of people to create a knowledge product in a decentralized way, without a person dictating who should contribute and who should not. Whose contribution to the knowledge product will be accepted or rejected is done by a consensus of all participating members. The system should allow transparently assessing and scoring the contribution of each of the participant to the final product. The system should also allow anonymous or semi-anonymous contribution.

A knowledge product is a product aiming at answering a specific policy issue or exploring an issue in depth to better understand it. For example a knowledge product can be built on the question: how can we fight desertification in a given country?

Our assumption is that the knowledge product can be mapped as set of concepts and relations between these concepts. We can therefore consider that the final product is a graph where we have a certain number of nodes and a certain number of relations between the nodes. We can see nodes as concepts and links as relations between concepts. For example, when we say “deforestation causes desertification”, deforestation is a node, desertification is a node and “causes” is a link between the two nodes. There can be a link between two nodes, a link between two links and a link between a node and a link. There are a fixed set of categories of links or nodes that can be used to build the knowledge product. These categories are proposed by the participants and start being used when a minimum level of consensus on their relevance has been reached in the network.

A participant can contribute to building the knowledge product in one of these ways:

• Propose a new category of node, which is equivalent to a new group of concepts. It will start be used by people in the next block if it receive a minimum score of relevance and originality.

• Propose a new node of existing category with attached text, which is equivalent to proposing to include a concept that is useful for understanding the topic in the knowledge product. It will immediately start being used. However it will receive relevance and originality scores from participants that will be aggregated at the end of the process.

• Propose a new category of link, which is equivalent to a group of relation between concepts. It will start be used by people in the next block if it receive a minimum score of relevance and originality.

• Propose a link of existing category with attached text. The link is between two nodes, two links, or a link and a node. For example if “deforestation” and “desertification” are already existing concepts in the knowledge product, a patrician can propose the link “deforestation causes desertification”. It’s a new knowledge piece that expresses a relation between two concepts.

• Propose a coefficient of relevance to the topic to any number of links or nodes as his vote on the question: how is the concept or relation relevant to the topic?

• Propose coefficient of similarity between two nodes or two links, trying to give his opinion on whether a new knowledge piece is already similar to another knowledge piece already in the document. 

• Propose a coefficient of originality to a node or a link proposed by someone else: to which extend does it add to the knowledge document?

The final knowledge product will be built based on the relevance, originality and similarity of the nodes. 

All participants have initial dotation in coins. Any contribution reduce their coins as a way to limit their participation and allow others to participate. A participant can modify a coefficient he has already given to a node ore link (originality, similarity, relevance) and he will lose only a fraction of what he lost when he has initially given the coefficient.

A participant can also transfer coins to another participant. We consider the operations described above as the transactions. Each transaction is adding knowledge to the product and reducing the dotation of the originator of the transaction. 

After a certain number of transactions, a block has to be created by a miner and added to the chain. The miner will only put transactions that are genuine and that have received enough consensus.

Once a consensus has been reached, the contribution of each participant will be computed based on the relevance, the originality and the similarity to other concepts of all nodes and links in the final graph. The more the person has contributed original knowledge pieces, the higher score he gets. He get less score if the knowledge piece is similar to another one that has already been included, or if the knowledge piece is not relevant to the question.

This is how it works: a topic is defined, those allowed to participate are given initial dotation. Then, they start proposing transactions. A miner at some point will take a maximum set of compatible transactions and create a block. The block freezes the situation and it is from the frozen situation that the originality of new contributions will be assessed. The miner is given score for contributing to secure the blockchain. We can use proof of stake to secure the blocks.  https://goo.gl/3nDnZY #DataScience #Cloud

Often during implementation of SAP Commercial Project Management one need to answer the question – which planning content is right one for my client’s business? Most implementation projects take one of the SAP delivered planning content as a basis and extend with rich extensibility tools that technology has to offer here.

Whole decision is quite central to project business needs of your client & may not know where to start. Following infographic helps to match the business needs & right planning content to start with.

Scenarios: 

This represents a set of business needs. SAP CPM Planning content can be broadly categorised into

* Output/ Productivity based planning – you estimate resources required for a specified units of service. For example you estimate resources for excavation of 230 m3 of trench. So in this case ‘Excavation’ is sort of ‘job’ that you perform and you may plan labour, equipment etc for this ‘job’

* Qty, Cost and Revenue Planning – you estimate various resources needed for project work packages / activity along project WBS. Simple example something like below. In this example, costs and revenues are calculated based on resource qty. SAP CPM Planning content is much more flexible and allows one to define many variants here.

WBS
Resource
Jun
July
Aug
Sept

Blue print
Business Process Consultant
48H
48H
30H
30H

Architect
100H
100H
80H
80H

Implementation
Technical Consultant Sr
200H
200H
100H
100H

  * Resource(employee) planning- you may specify the candidates or employees for each resource in the above table OR you may specify skill profile needed, so that you can communicate the skills and hourly demand to external resource management solutions such as SAP MRS

Usecases

This represents set of user actions such as planning(budgeting), forecasting, change request planning & reporting on plan, forecast and change requests.

Frequency

Represents the planning time buckets. Eg: ‘Month’ as a time bucket in above table. Various planning time buckets are available.This time bucket can be flexibly used for months, weeks etc.

I strongly recommend using ‘Fiscal(sometimes referred as periodic within SAP documentation) time bucket’ from flexibility point of view [for e.g.: your needs may change from months to weeks for certain types of projects] and saves efforts in extending your custom extensions into multiple sets of planning content

Definition of fiscal period can be done within transaction OB29.

Query

Input ready query can be used create planning application by inserting them into SAP Analysis Office workbook or Lumira Designer Application. SAP CPM Input ready queries are defined for combination of scenarios, usecases and frequency. Available planning content is documented within product documentation.

https//help.sap.com

For latest release [S/4HANA 1709], you can find them following this link 

Ready for some hands-on exercise?

Let’s try to figure out a planning query for qty, cost and revenue planning on a monthly planning bucket [I will use fiscal frequency as this is more flexible]

With below screenshot one could find technical name of planning content and use them readily within a workbook.

That’s it for now. Stay tuned for next blog on how to insert this query into an SAP Analysis office workbook to create a planning application. http://bit.ly/2GmGtoq #SAP #SAPCloud #AI