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6 Ways Managers Can Engage And Retain New Hires

Charles Rogel

Starting a job is an exciting and scary moment for new hires. The first 90 days of employment are a critical time when they develop lasting opinions about their workplace that will affect their future performance. When handled well, the onboarding experience sets up an employee for success that will pay strong dividends to the company.

The good news

Our employee engagement survey benchmark shows new employees are more engaged in their work and committed to the company than other employees. When we looked at engagement levels of first-year employees in our employee engagement survey database for 2015, we found 82% of employees with less than one year of tenure are engaged, compared to 75% of longer-tenured employees. Looking at the same data, we also found their commitment to stay (retention) is higher than other tenure groups: 74% compared to 70%.

But the employee experience for a new hire can be shaky during the first year. New employees are learning their job, understanding how the company works, and building relationships with coworkers, all while trying to establish a reputation as a top contributor. An employee’s direct manager has a huge influence over these factors.

As part of a successful onboarding experience, here are six ways managers can boost retention and engagement with new hires.

Have career development conversations

During the first week, talk with new hires about their career aspirations. Find out where they want to be in five years, and help them see how they can achieve their goals in the new company. Identify learning opportunities and map out a rough timeline to hit certain milestones. Review these plans every quarter during a one-on-one.

Involve them in social activities

Integrating socially can be one of the most stressful parts of starting a new job. Making new friends is difficult for some people, so take an active role in including new hires in company activities and group lunches. Introduce them to people they will need to work with to be successful in their role.

Ensure they have the training they need

Many new hires are provided “on-the-job training,” which essentially means “start doing the work and figure it out on your own.” This type of experience can quickly lead to frustration and burn-out for new employees. Make sure they receive the formal training they need to feel confident in their ability to be successful.

Provide the necessary tools and resources

Are your new hires given the “hand-me-down” computer to do their job? If so, they may struggle to get it to work and may be reluctant to say anything about it. As a manager, use your influence to get them the tools they need to be successful.

Help align new hires with the company culture and mission

New employees are excited to make an impression and contribute in their new roles. Show them how their work makes a difference and drives organizational success. As they see meaning and impact in their work, they will be more committed to stay and deliver results.

Solicit their feedback

Use a confidential new-hire survey to gather feedback from employees during their first year. When speaking with their managers, new hires may not be completely honest about all aspects of their job. A new hire survey administered at 30, 60, and 90 days will provide a comprehensive look at their employee experience. Because the survey is confidential, results will be summarized for all new hires, but the data will give you important insights into problems that may not come up in your one-on-one conversations.

As a manager, don’t compromise the high level of engagement new hires experience during their first year by being careless about the onboarding experience. When onboarding is done well, new hires will get up to speed faster and deliver stronger results.

For more insight on the future of the workplace, see 5 Reasons To Ditch Performance Reviews In 2017.

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How To Design Your Company’s Digital Transformation

Sam Yen

The September issue of the Harvard Business Review features a cover story on design thinking’s coming of age. We have been applying design thinking within SAP for the past 10 years, and I’ve witnessed the growth of this human-centered approach to innovation first hand.

Design thinking is, as the HBR piece points out, “the best tool we have for … developing a responsive, flexible organizational culture.”

This means businesses are doing more to learn about their customers by interacting directly with them. We’re seeing this change in our work on d.forum — a community of design thinking champions and “disruptors” from across industries.

Meanwhile, technology is making it possible to know exponentially more about a customer. Businesses can now make increasingly accurate predictions about customers’ needs well into the future. The businesses best able to access and pull insights from this growing volume of data will win. That requires a fundamental change for our own industry; it necessitates a digital transformation.

So, how do we design this digital transformation?

It starts with the customer and an application of design thinking throughout an organization – blending business, technology and human values to generate innovation. Business is already incorporating design thinking, as the HBR cover story shows. We in technology need to do the same.

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Design thinking plays an important role because it helps articulate what the end customer’s experience is going to be like. It helps focus all aspects of the business on understanding and articulating that future experience.

Once an organization is able to do that, the insights from that consumer experience need to be drawn down into the business, with the central question becoming: What does this future customer experience mean for us as an organization? What barriers do we need to remove? Do we need to organize ourselves differently? Does our process need to change – if it does, how? What kind of new technology do we need?

Then an organization must look carefully at roles within itself. What does this knowledge of the end customer’s future experience mean for an individual in human resources, for example, or finance? Those roles can then be viewed as end experiences unto themselves, with organizations applying design thinking to learn about the needs inherent to those roles. They can then change roles to better meet the end customer’s future needs. This end customer-centered approach is what drives change.

This also means design thinking is more important than ever for IT organizations.

We, in the IT industry, have been charged with being responsive to business, using technology to solve the problems business presents. Unfortunately, business sometimes views IT as the organization keeping the lights on. If we make the analogy of a store: business is responsible for the front office, focused on growing the business where consumers directly interact with products and marketing; while the perception is that IT focuses on the back office, keeping servers running and the distribution system humming. The key is to have business and IT align to meet the needs of the front office together.

Remember what I said about the growing availability of consumer data? The business best able to access and learn from that data will win. Those of us in IT organizations have the technology to make that win possible, but the way we are seen and our very nature needs to change if we want to remain relevant to business and participate in crafting the winning strategy.

We need to become more front office and less back office, proving to business that we are innovation partners in technology.

This means, in order to communicate with businesses today, we need to take a design thinking approach. We in IT need to show we have an understanding of the end consumer’s needs and experience, and we must align that knowledge and understanding with technological solutions. When this works — when the front office and back office come together in this way — it can lead to solutions that a company could otherwise never have realized.

There’s different qualities, of course, between front office and back office requirements. The back office is the foundation of a company and requires robustness, stability, and reliability. The front office, on the other hand, moves much more quickly. It is always changing with new product offerings and marketing campaigns. Technology must also show agility, flexibility, and speed. The business needs both functions to survive. This is a challenge for IT organizations, but it is not an impossible shift for us to make.

Here’s the breakdown of our challenge.

1. We need to better understand the real needs of the business.

This means learning more about the experience and needs of the end customer and then translating that information into technological solutions.

2. We need to be involved in more of the strategic discussions of the business.

Use the regular invitations to meetings with business as an opportunity to surface the deeper learning about the end consumer and the technology solutions that business may otherwise not know to ask for or how to implement.

The IT industry overall may not have a track record of operating in this way, but if we are not involved in the strategic direction of companies and shedding light on the future path, we risk not being considered innovation partners for the business.

We must collaborate with business, understand the strategic direction and highlight the technical challenges and opportunities. When we do, IT will become a hybrid organization – able to maintain the back office while capitalizing on the front office’s growing technical needs. We will highlight solutions that business could otherwise have missed, ushering in a digital transformation.

Digital transformation goes beyond just technology; it requires a mindset. See What It Really Means To Be A Digital Organization.

This story originally appeared on SAP Business Trends.

Top image via Shutterstock

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Sam Yen

About Sam Yen

Sam Yen is the Chief Design Officer for SAP and the Managing Director of SAP Labs Silicon Valley. He is focused on driving a renewed commitment to design and user experience at SAP. Under his leadership, SAP further strengthens its mission of listening to customers´ needs leading to tangible results, including SAP Fiori, SAP Screen Personas and SAP´s UX design services.

How Productive Could You Be With 45 Minutes More Per Day?

Michael Rander

Chances are that you are already feeling your fair share of organizational complexity when navigating your current company, but have you ever considered just how much time is spent across all companies on managing complexity? According to a recent study by the Economist Intelligence Unit (EIU), the global impact of complexity is mind-blowing – and not in a good way.

The study revealed that 38% of respondents spent 16%-25% of their time just dealing with organizational complexity, and 17% spent a staggering 26%-50% of their time doing so. To put that into more concrete numbers, in the US alone, if executives could cut their time spent managing complexity in half, an estimated 8.6 million hours could be saved a week. That corresponds to 45 minutes per executive per day.

The potential productivity impact of every executive having 45 minutes more to work every single day is clearly significant, and considering that 55% say that their organization is either very or extremely complex, why are we then not making the reduction of complexity one or our top of mind issues?

The problem is that identifying the sources of complexity is complex in of itself. Key sources of complexity include organizational size, executive priorities, pace of innovation, decision-making processes, vastly increasing amounts of data to manage, organizational structures, and the pure culture of the company. As a consequence, answers are not universal by any means.

That being said, the negative productivity impact of complexity, regardless of the specific source, is felt similarly across a very large segment of the respondents, with 55% stating that complexity has taken a direct toll on profitability over the past three years.  This is such a serious problem that 8% of respondents actually slowed down their company growth in order to deal with complexity.

So, if complexity oftentimes impacts productivity and subsequently profitability, what are some of the more successful initiatives that companies are taking to combat these effects? Among the answers from the EIU survey, the following were highlighted among the most likely initiatives to reduce complexity and ultimately increase productivity:

  • Making it a company-wide goal to reduce complexity means that the executive level has to live and breathe simplification in order for the rest of the organization to get behind it. Changing behaviors across the organization requires strong leadership, commitment, and change management, and these initiatives ultimately lead to improved decision-making processes, which was reported by respondents as the top benefit of reducing complexity. From a leadership perspective this also requires setting appropriate metrics for measuring outcomes, and for metrics, productivity and efficiency were by far the most popular choices amongst respondents though strangely collaboration related metrics where not ranking high in spite of collaboration being a high level priority.
  • Promoting a culture of collaboration means enabling employees and management alike to collaborate not only within their teams but also across the organization, with partners, and with customers. Creating cross-functional roles to facilitate collaboration was cited by 56% as the most helpful strategy in achieving this goal.
  • More than half (54%) of respondents found the implementation of new technology and tools to be a successful step towards reducing complexity and improving productivity. Enabling collaboration, reducing information overload, building scenarios and prognoses, and enabling real-time decision-making are all key issues that technology can help to reduce complexity at all levels of the organization.

While these initiatives won’t help everyone, it is interesting to see that more than half of companies believe that if they could cut complexity in half they could be at least 11%-25% more productive. That nearly one in five respondents indicated that they could be 26%-50% more productive is a massive improvement.

The question then becomes whether we can make complexity and its impact on productivity not only more visible as a key issue for companies to address, but (even more importantly) also something that every company and every employee should be actively working to reduce. The potential productivity gains listed by respondents certainly provide food for thought, and few other corporate activities are likely to gain that level of ROI.

Just imagine having 45 minutes each and every day for actively pursuing new projects, getting innovative, collaborating, mentoring, learning, reducing stress, etc. What would you do? The vision is certainly compelling, and the question is are we as companies, leaders, and employees going to do something about it?

To read more about the EIU study, please see:

Feel free to follow me on Twitter: @michaelrander

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About Michael Rander

Michael Rander is the Global Research Director for Future Of Work at SAP. He is an experienced project manager, strategic and competitive market researcher, operations manager as well as an avid photographer, athlete, traveler and entrepreneur. Share your thoughts with Michael on Twitter @michaelrander.

How AI Can End Bias

Yvonne Baur, Brenda Reid, Steve Hunt, and Fawn Fitter

We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. When we talk about handing decisions off to artificial intelligence (AI), we expect it to do the same, only better.

Machine learning does, in fact, have the potential to be a tremendous force for good. Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information. AI, on the other hand, can be taught to filter irrelevancies out of the decision-making process, pluck the most suitable candidates from a haystack of résumés, and guide us based on what it calculates is objectively best rather than simply what we’ve done in the past.

In other words, AI has the potential to help us avoid bias in hiring, operations, customer service, and the broader business and social communities—and doing so makes good business sense. For one thing, even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.

Beyond managing risk related to legal and regulatory issues, though, there’s a broader argument for tackling bias: in a relentlessly competitive and global economy, no organization can afford to shut itself off from broader input, more varied experiences, a wider range of talent, and larger potential markets.

That said, the algorithms that drive AI don’t reveal pure, objective truth just because they’re mathematical. Humans must tell AI what they consider suitable, teach it which information is relevant, and indicate that the outcomes they consider best—ethically, legally, and, of course, financially—are those that are free from bias, conscious or otherwise. That’s the only way AI can help us create systems that are fair, more productive, and ultimately better for both business and the broader society.

Bias: Bad for Business

When people talk about AI and machine learning, they usually mean algorithms that learn over time as they process large data sets. Organizations that have gathered vast amounts of data can use these algorithms to apply sophisticated mathematical modeling techniques to see if the results can predict future outcomes, such as fluctuations in the price of materials or traffic flows around a port facility. Computers are ideally suited to processing these massive data volumes to reveal patterns and interactions that might help organizations get ahead of their competitors. As we gather more types and sources of data with which to train increasingly complex algorithms, interest in AI will become even more intense.

Using AI for automated decision making is becoming more common, at least for simple tasks, such as recommending additional products at the point of sale based on a customer’s current and past purchases. The hope is that AI will be able to take on the process of making increasingly sophisticated decisions, such as suggesting entirely new markets where a company could be profitable, or finding the most qualified candidates for jobs by helping HR look beyond the expected demographics.

As AI takes on these increasingly complex decisions, it can help reduce bias, conscious or otherwise. By exposing a bias, algorithms allow us to lessen the impact of that bias on our decisions and actions. They enable us to make decisions that reflect objective data instead of untested assumptions; they reveal imbalances; and they alert people to their cognitive blind spots so they can make more accurate, unbiased decisions.

Imagine, for example, a major company that realizes that its past hiring practices were biased against women and that would benefit from having more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.

AI can also support people in making less-biased decisions. For example, a company is considering two candidates for an influential management position: one man and one woman. The final hiring decision lies with a hiring manager who, when they learn that the female candidate has a small child at home, assumes that she would prefer a part-time schedule.

That assumption may be well intentioned, but it runs counter to the outcome the company is looking for. An AI could apply corrective pressure by reminding the hiring manager that all qualifications being equal, the female candidate is an objectively good choice who meets the company’s criteria. The hope is that the hiring manager will realize their unfounded assumption and remove it from their decision-making process.

At the same time, by tracking the pattern of hiring decisions this manager makes, the AI could alert them—and other people in HR—that the company still has some remaining hidden biases against female candidates to address.

Look for Where Bias Already Exists

In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.

There are many scenarios where humans can collaborate with AI to prevent or even reverse bias, says Jason Baldridge, a former associate professor of computational linguistics at the University of Texas at Austin and now co-founder of People Pattern, a startup for predictive demographics using social media analytics. In the highly regulated financial services industry, for example, Baldridge says banks are required to ensure that their algorithmic choices are not based on input variables that correlate with protected demographic variables (like race and gender). The banks also have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups, he says. What’s more, they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. As a result, banks are more evenhanded in their lending.

Code Is Only Human

The reason for these checks and balances is clear: the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models. Because humans are prone to bias, we have to be careful that we are neither simply confirming existing biases nor introducing new ones when we develop AI models and feed them data.

“From the perspective of a business leader who wants to do the right thing, it’s a design question,” says Cathy O’Neil, whose best-selling book Weapons of Math Destruction was long-listed for the 2016 National Book Award. “You wouldn’t let your company design a car and send it out in the world without knowing whether it’s safe. You have to design it with safety standards in mind,” she says. “By the same token, algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” (To learn more from O’Neil about transparency in algorithms, read Thinkers in this issue.)

Don’t Do What You’ve Always Done

To eliminate bias, you must first make sure that the data you’re using to train the algorithm is itself free of bias, or, rather, that the algorithm can recognize bias in that data and bring the bias to a human’s attention.

SAP has been working on an initiative that tackles this issue directly by spotting and categorizing gendered terminology in old job postings. Nothing as overt as No women need apply, which everyone knows is discriminatory, but phrases like outspoken and aggressively pursuing opportunities, which are proven to attract male job applicants and repel female applicants, and words like caring and flexible, which do the opposite.

Once humans categorize this language and feed it into an algorithm, the AI can learn to flag words that imply bias and suggest gender-neutral alternatives. Unfortunately, this de-biasing process currently requires too much human intervention to scale easily, but as the amount of available de-biased data grows, this will become far less of a limitation in developing AI for HR.

Similarly, companies should look for specificity in how their algorithms search for new talent. According to O’Neil, there’s no one-size-fits-all definition of the best engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.

Look Beyond the Obvious

AI could be invaluable in radically reducing deliberate and unconscious discrimination in the workplace. However, the more data your company analyzes, the more likely it is that you will deal with stereotypes, O’Neil says. If you’re looking for math professors, for example, and you load your hiring algorithm with all the data you can find about math professors, your algorithm may give a lower score to a black female candidate living in Harlem simply because there are fewer black female mathematicians in your data set. But if that candidate has a PhD in math from Cornell, and if you’ve trained your AI to prioritize that criterion, the algorithm will bump her up the list of candidates rather than summarily ruling out a potentially high-value hire on the spurious basis of race and gender.

To further improve the odds that AI will be useful, companies have to go beyond spotting relationships between data and the outcomes they care about. It doesn’t take sophisticated predictive modeling to determine, for example, that women are disproportionately likely to jump off the corporate ladder at the halfway point because they’re struggling with work/life balance.

Many companies find it all too easy to conclude that women simply aren’t qualified for middle management. However, a company committed to smart talent management will instead ask what it is about these positions that makes them incompatible with women’s lives. It will then explore what it can change so that it doesn’t lose talent and institutional knowledge that will cost the company far more to replace than to retain.

That company may even apply a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, making the workforce more ethnically diverse, or improving retention statistics, and Y is redefining job responsibilities with greater flexibility, hosting recruiting events in communities of color, or redesigning benefits packages based on what similar companies offer.

Context Matters—and Context Changes

Even though AI learns—and maybe because it learns—it can never be considered “set it and forget it” technology. To remain both accurate and relevant, it has to be continually trained to account for changes in the market, your company’s needs, and the data itself.

Sources for language analysis, for example, tend to be biased toward standard American English, so if you’re building models to analyze social media posts or conversational language input, Baldridge says, you have to make a deliberate effort to include and correct for slang and nonstandard dialects. Standard English applies the word sick to someone having health problems, but it’s also a popular slang term for something good or impressive, which could lead to an awkward experience if someone confuses the two meanings, to say the least. Correcting for that, or adding more rules to the algorithm, such as “The word sick appears in proximity to positive emoji,” takes human oversight.

Moving Forward with AI

Today, AI excels at making biased data obvious, but that isn’t the same as eliminating it. It’s up to human beings to pay attention to the existence of bias and enlist AI to help avoid it. That goes beyond simply implementing AI to insisting that it meet benchmarks for positive impact. The business benefits of taking this step are—or soon will be—obvious.

In IDC FutureScapes’ webcast “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions,” research director David Schubmehl predicted that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology. However, Schubmehl also speculated that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.

Clearly, organizations that can address this concern explicitly will have a competitive advantage, but simply stating their commitment to using AI for good may not be enough. They also may wish to support academic efforts to research AI and bias, such as the annual Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop, which was held for the third time in November 2016.

O’Neil, who blogs about data science and founded the Lede Program for Data Journalism, an intensive certification program at Columbia University, is going one step further. She is attempting to create an entirely new industry dedicated to auditing and monitoring algorithms to ensure that they not only reveal bias but actively eliminate it. She proposes the formation of groups of data scientists that evaluate supply chains for signs of forced labor, connect children at risk of abuse with resources to support their families, or alert people through a smartphone app when their credit scores are used to evaluate eligibility for something other than a loan.

As we begin to entrust AI with more complex and consequential decisions, organizations may also want to be proactive about ensuring that their algorithms do good—so that their companies can use AI to do well. D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Yvonne Baur is Head of Predictive Analytics for Sap SuccessFactors solutions.

Brenda Reid is Vice President of Product Management for Sap SuccessFactors solutions.

Steve Hunt is Senior Vice President of Human Capital Management Research for Sap SuccessFactors solutions.

Fawn Fitter is a freelance writer specializing in business and technology.

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Next-Generation, Real-Time Data Warehouse: Bringing Analytics To Data

Iver van de Zand

Imagine the following situation: you are analyzing and gathering insights about product sales performance and wonder why a certain area in your country is doing better than others. You deep dive, slice, dice, and use different perspectives to analyze, but can’t find the answer to why sales are better for that region.

You conclude you need data that is not available in your corporate systems. Some geographical data that is available through Hadoop might answer your question. How can you get this information and quickly analyze it all?

Bring analytics to data

If we don’t want to go the traditional route of specifying, remodeling the data warehouse, and uploading and testing data, we’d need a whole new way of modern data warehousing. What we ultimately need is a kind of semantics that allows us to remodel our data warehouse in real time and on the fly – semantics that allows decision makers to leave the data where it is stored without populating it into the data warehouse. What we really need is a way to bring our analytics to data, instead of the other way around.

So our analytics wish list would be:

  • Access to the data source on the fly
  • Ability to remodel the data warehouse on the fly
  • No replication of data; the data stays where it is
  • Not losing time with data-load jobs
  • Analytical processing done in the moment with pushback to an in-memory computing platform
  • Drastic reduction of data objects to be stored and maintained
  • Elimination of aggregates

Traditional data warehousing is probably the biggest hurdle when it comes to agile business analytics. Though modern analytical tools perfectly add data sources on the fly and blend different data sources, these components are still analytical tools. When additional data must be available for multiple users or is huge in scale and complexity, analytical tools lack the computing power and scalability needed. It simply doesn’t make sense to blend them individually when multiple users require the same complex, additional data.

A data warehouse, in this case, is the answer. However, there is still one hurdle to overcome: A traditional data warehouse requires a substantial effort to adjust to new data needs. So we add to our wish list:

  • Adjust and adapt the modeling
  • Develop load and transformation script
  • Assign sizing
  • Setup scheduling and linage
  • Test and maintain

In 2016, the future of data warehousing began. In-memory technology with smart, native, and real-time access moved information from analytics to the data warehouse, as well as the data warehouse to core in-memory systems. Combined with pushback technology, where analytical calculations are pushed back onto an in-memory computing platform, analytics is brought back to data. End-to-end in-memory processing has become the reality, enabling true agility. And end-to-end processing is ready for the Internet of Things at the petabyte scale.

Are we happy with this? Sure, we are! Does it come as a surprise? Of course, not! Digital transformation just enabled it!

Native, real-time access for analytics

What do next-generation data warehouses bring to analytics? Well, they allow for native access from top-end analytics components through the data warehouse and all the way to the core in-memory platform with our operational data. Even more, this native access is real-time. Every analytics-driven interaction from an end-user generates calculations. With the described architecture, these calculations are massively pushed back to the core platform where our data resides.

The same integrated architecture is also a game changer when it comes to agility and data optimization. When new, complex data is required, it can be added without data replication. Since there is no data replication, the data warehouse modeling can be done on the fly, leveraging the semantics. We no longer have to model, create, and populate new tables and aggregates when additional data is required in the data warehouse, because there are no new tables needed! We only create additional semantics, and this can be done on the fly.

Learn why you need to put analytics into your business processes in the free eBook How to Use Algorithms to Dominate Your Industry.

This article appeared on Iver van de Zand.

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Iver van de Zand

About Iver van de Zand

Iver van de Zand is a Business Analytics Leader at SAP responsible for Business Analytics with a special attention towards Business Intelligence Suite, Lumira and Predictive Analytics.