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A number of changes in the contemporary data landscape have affected the implementation of data governance. The normalization of big data has resulted in a situation in which such deployments are so common that they’re merely considered a standard part of data management. The confluence of technologies largely predicated on big data—cloud, mobile and social—are gaining similar prominence, transforming the expectations of not only customers but business consumers of data.

Consequently, the demands for big data governance are greater than ever, as organizations attempt to implement policies to reflect their corporate values and sate customer needs in a world in which increased regulatory consequences and security breaches are not aberrations.

The most pressing developments for big data governance in 2016 include three dominant themes. Organizations need to enforce it outside the corporate firewalls via the cloud, democratize the level of data stewardship requisite for the burgeoning self-service movement, and provide metadata and semantic consistency to negate the impact of silos while promoting sharing of data across the enterprise.

These objectives are best achieved with a degree of foresight and stringency that provides a renewed emphasis on modeling in its myriad forms. According to TopQuadrant co-founder, executive VP and director of TopBraid Technologies Ralph Hodgson, “What you find is the meaning of data governance is shifting. I sometimes get criticized for saying this, but it’s shifting towards a sense of modeling the enterprise.”

In the Cloud

Perhaps the single most formidable challenge facing big data governance is accounting for the plethora of use cases involving the cloud, which appears tailored for the storage and availability demands of big data deployments. These factors, in conjunction with the analytics options available from third-party providers, make utilizing the cloud more attractive than ever. However, cloud architecture challenges data governance in a number of ways including:

• Semantic modeling: Each cloud application has its own semantic model. Without dedicated governance measures on the part of an organization, integrating those different models can hinder data’s meaning and its reusability.
• Service provider models: Additionally, each cloud service provider has its own model which may or may not be congruent with enterprise models for data. Organizations have to account for these models as well as those at the application level.
• Metadata: Applications and cloud providers also have disparate metadata standards which need to be reconciled. According to Tamr Global Head of Strategy, Operations and Marketing Nidhi Aggarwal, “Seeing the metadata is important from a governance standpoint because you don’t want the data available to anybody. You want the metadata about the data transparent.” Vendor lock-in in the form of proprietary metadata issued by providers and their applications can be a problem too—especially since such metadata can encompass an organization’s so that it effectively belongs to the provider.

Rectifying these issues requires a substantial degree of planning prior to entering into service level agreements. Organizations should consider both current and future integration plans and their ramifications for semantics and metadata, which is part of the basic needs assessment that accompanies any competent governance program. Business input is vital to this process. Methods for addressing these cloud-based points of inconsistency include transformation and writing code, or adopting enterprise-wide semantic models via ontologies, taxonomies, and RDF graphs. The critical element is doing so in a way that involves the provider prior to establishing service.

The Democratization of Data Stewardship

The democratization of big data is responsible for an emergence of what Gartner refers to as ‘citizen stewardship’ in two capital ways. The popularity of data lakes and the availability of data preparation tools with cognitive computing capabilities are empowering end users to assert more control over their data. The result is a shifting from the centralized model of data stewardship (which typically encompassed stewards from both the business and IT, the former in accordance to domains) to a decentralized one in which virtually everyone actually using data plays a role in its stewardship.

Both preparation tools and data lakes herald this movement by giving end users the opportunity to perform data integration. Machine learning technologies inform the former and can identify which data is best integrated with others on an application or domain-wide basis. The celerity of this self-service access and integration to data necessitates that the onus of integrating data in accordance to governance policy falls on the end user. Preparation tools can augment that process by facilitating ETL and other forms of action with machine learning algorithms, which can maintain semantic consistency.

Data lakes equipped with semantic capabilities can facilitate a number of preparation functions from initial data discovery to integration while ensuring the sort of metadata and semantic consistency for proper data governance. Regardless, “if you put data in a data lake, there still has to be some metadata associated with it,” MapR Chief Marketing Officer Jack Norris explained. “You need some sort of schema that’s defined so you can accomplish self-service.”

Metadata and Semantic Consistency

No matter what type of architecture is employed (either cloud or on-premise), consistent metadata and semantics represent the foundation of secure governance once enterprise wide policies based on business objectives are formulated. As noted by Franz CEO Jans Aasman, “That’s usually how people define data governance: all the processes that enable you to have more consistent data”. Perhaps the most thorough means of ensuring consistency in these two aspects of governance involves leveraging a data lake or single repository enriched with semantic technologies. The visual representation of data elements on an RDF graph is accessible for end user consumption, while semantic models based on ontological descriptions of data elements clarify their individual meanings. These models can be mapped to metadata to grant uniformity in this vital aspect of governance and provide semantic consistency on diverse sets of big data.

Alternatively, it is possible to achieve metadata consistency via processes instead of technologies. Doing so is more tenuous, yet perhaps preferable to organizations still utilizing a silo approach among different business domains. Sharing and integrating that data is possible through the means of an enterprise-wide governance council with business membership across those domains, which rigorously defines and monitors metadata attributes so that there is still a common semantic model across units. This approach might behoove less technologically savvy organizations, although the sustainment of such councils could become difficult. Still, this approach results in consistent metadata and semantic models on disparate sets of big data.

Enterprise Modeling

The emphasis on modeling that is reflected in all of these trends substantiates the viewpoint that effective big data governance requires strident modeling. Moreover, it is important to implement at a granular level so that data is able to be reused and maintain its meaning across different technologies, applications, business units, and personnel changes. The degree of prescience and planning required to successfully model the enterprise to ensure governance objectives are met will be at the forefront of governance concerns in 2016, whether organizations are seeking new data management solutions or refining established ones. In this respect, governance is actually the foundation upon which data management rests. According to Cambridge Semantics president Alok Prasad, “Even if you are the CEO, you will not go against your IT department in terms of security and governance. Even if you can get a huge ROI, if the governance and security are not there you will not adopt a solution.”

Originally Posted at: 2016 Trends in Big Data Governance: Modeling the Enterprise

News and commentary about health and biotech data:

How data science is transforming cancer treatment scheduling: Scheduling appointments is a problem that many electronic systems are not able to handle efficiently. New holistic approaches inspired by manufacturing are helping to improve this process.

Data Analytics May Keep Cancer Patients out of Emergency Departments: Researchers at the UPenn school of medicine are developing a model to predict when patients need emergency care, as well as best practices for when they need such care.

Why practices are struggling to exchange records: A recent survey found some key issues in the exchange of electronic health records, including a lack of confidence in the technology and difficulties transferring data across different systems.

Pharmacist and drug associations want better data on medicine shortages: The associations called for more up-to-date information about possible shortages from providers.

Secrets of Life in a Spoonful of Blood: Researchers have increasingly powerful tools to study fetal development, including sequencing DNA found in the mother’s blood.

Originally Posted at: February 13, 2017 Health and Biotech analytics news roundup by pstein

Everyone talks about how important it is to be on Social Media.  But how do you use it to gather more customers?  Well, Kathleen Davis has shared some information compiled by Wishpond.

Did you know that 77% of B2C have found new customers through Facebook, while LinkedIn has proven significant for B2B. Try 277% more effective than Facebook. Those are some outstanding numbers!

Check out the rest in the Infographic below.

source

Source

I came across Pitch Deck used by Buffer co-founders Joel Gascoigne and Leo Widrich to raise $500k round. This deck has lots of good information and it could be really useful for other startups seeking to raise capital.

Why it is relevant for most of startups? Because buffer founders were also first timers.

One of the big no-no’s we’ve learnt about early on in Silicon Valley is to publicly share the pitchdeck you’ve used to raise money. At least, not before you’ve been acquired or failed or in any other way been removed from stage. That’s a real shame, we thought. Sharing the actual slidedeck we used (and one, that’s not 10 years old) is by far one of the most useful things for others to learn from. In fact, both Joel and I have privately shared the deck with fledging founders to help them with their fundraising. On top of that, our case study is hopefully uniquely insightful for lots of people. Here is why:
Half a million is not a crazy amount: It’s therefore hopefully an example that helps the widest range of founders trying to raise money.
Both Joel and myself are first-timers: We couldn’t just throw big names onto a slideshow and ride with it. We had to test and change the flow and deck a lot.

As a summary: this deck is build upto one key slide: Traction.

So without further ado – have a look at their pitch deck.

ps: If you want to read more? check out OnStartups.com

Source by v1shal

Companies across all industries depend more and more on analytics and insights to run their businesses profitably. But, attracting, managing and retaining talented personnel to execute on those strategies remains a challenge. This is not the case for consumer products heavyweight The Procter & Gamble Company (P&G), which has been at the top of its analytics game for 50 years now.

During the 2014 Retail/Consumer Goods Analytics Summit, Glenn Wegryn, retired associate director of analytics for P&G, shared best practices for building the talent capabilities required to ensure success. A leadership council is in charge of sharing analytics best practices across P&G — breaking down silos to make sure the very best talent is being leveraged to solve the company’s most pressing business issues.

So, what are the characteristics of a great data analyst and where can you find them?

“I always look for people with solid quantitative backgrounds because that is the hardest thing to learn on the job,” said Wegryn.

Combine that with mature communication skills and a talent for business acumen and you’ve got the perfect formula for a great data analyst.

When it comes to sourcing analytics, Wegryn says companies have an important strategic decision to make: Do you build it internally, leveraging resources like consultants and universities? Do you buy it from a growing community of technology solution providers? Or, do you adopt a hybrid model?

“Given the explosion of business analytics programs across the country, your organization should find ample opportunities to tap into those resources,” advised Wegryn.

To retain and nurture your organization’s business analysts, Wegryn recommended creating a career path that grows and the importance of encouraging talented personnel internally until they reach a trusted CEO advisory role.

Wegryn also shared key questions an organization should ask to unleash the value of analytics, and suggested that analytics should always start and end with a decision.

“You make a decision in business that leads to action that gleans insights that leads to another decision,” he said. “While the business moves one way, the business analyst works backward in a focused, disciplined and controlled manner.”

Perhaps most importantly, the key to building the talent capability to ensure analytics success came from P&G’s retired chairman, president and CEO Bob McDonald: “… having motivation from the top helps.”

Wegryn agreed: “It really helps when the person at the top of the chain is driven on data.”

The inaugural Retail & Consumer Goods Analytics Summit event was held September 11-12, 2014 at the W Hotel in San Diego, California. The conference featured keynotes from retail and consumer goods leaders, peer-to-peer exchanges and relationship building.

Article originally appeared HERE.

Originally Posted at: Best Practices For Building Talent In Analytics by analyticsweekpick

Big data is a game changer. But is big data a tech or marketing tool? The answer is both. Big data plays a role in marketing campaigns and gathering insight. To utilize big data most effectively, marketers must understand its use in the following roles.

Trends and Predictive Analytics

Salesforce.com highlights the role of Google Trends as a key player. Google Trends takes big data analysis to the big leagues. It cuts out all of the current trending topics and focuses on the ones with the greatest reach, quantifying the frequency of each searched term and comparing it to the total amount of searches. This helps marketers to expand their reach more than ever. It’s easy to get complacent with search words you already know. With tools like Google Trends, you can find new trends and new markets. Think about the growth of video screens in places of worship, for example. Marketers consistently focus on entertainment and office environments that need large video screens. But by using tools like Google trends can you see the demand and need for services outside of your usual targets and grow to meet changing needs and expanding your reach.

Predictive analytics is another big data strategy. Predictive analytics is an area of data mining that extracts information from existing data sets to determine behavior patterns and predict future trends. Forbes talks about its progressive and aggressive nature. It looks at scores of historical data and analyzes it with incredible speeds. It can literally pinpoint the exact process that makes for successful leads. Predictive data allows marketers to learn more about their target customers than ever before. Using predictive data analytics will drastically change your selling cycles. Instead of waiting for your clients to request additional services, you can predict their needs.

Persona Creation

Much like SEO techniques in the past, big data will change the way we create and use buyer personas. Buyer personas are generally use in marketing efforts to pinpoint a certain customer. According to Salesforce.com, companies usually create personas from data gleaned from their websites and from feedback from sales teams and call centers, which misses huge pools of data. Using big data, social media, blog posts, and marketing campaigns can be geared to more specific customers by targeting demographics in much larger ways. A multimarket approach breaks the generic mould. A small business owner will use your services if they see blog posts geared to them; this is the same for other markets like health care and education. By gearing posts to different people, you gain loyalty from a larger group of markets.

Personalization and Customization

Every marketer knows the effectiveness of personalization and customization. When your customers think you are talking to them directly, they become loyal to your brand. It’s also important to make sure you send the right message at the right time to secure customer loyalty. Using big data, you can see who’s interacting with your brand in real time. Armed with this information, marketers can send personalized and customized responses. You can merge big data with existing CRM practices, and keep tabs on trends of potential buyers as well as current customers. As you track these patterns, you can send customers individualized content creating greater customer retention. Let’s say your customer is looking for new services for the latest project they are taking on. By sending personalized content, you create a relationship that makes them feel like you care about their success. This is how you create loyalty.

Marketers who ignore big data, even small data, will be left behind. Don’t let that be you.

To read the original article on HuffPost Business, click here.

Source: What Marketers Really Need To Know About Big Data by analyticsweekpick