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Data Governance for Enterprises: Important Principles for Value Generation

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Click to learn more about author Deepak Gupta.

Data Governance
has emerged as one of the top priorities for organizations across the globe.
Given this reality, organizations are handling their data consistently to
support business outcomes.

So, what is Data Governance for
enterprises, and why is it important?

Data Governance is a set of laws implemented in an organization for deciding the control and authority over their data assets, which means it goes a long way in affecting the tactical and operational decisions in most enterprises.

Usually, a proper Data Governance program involves using agreed-upon models and determining who can use the company’s data assets and under
what circumstances. 

Data executives across the globe have begun prioritizing enterprise Data Governance, given that regulations like GDPR and CCPA are increasingly being implemented.

Efficient Data Governance is crucial for the integrity, security,
availability, and usability of the data. So, it makes a lot of sense to ensure
that your organization has the appropriate certifications to get it
right. 

Below see a list of
certifications that should be included for proper Data Governance:

  • AICPA SOC 2 (Type II): This certification makes sure that the data is secure, available, and that it maintains its integrity. 
  • ISAE 3000: This is a certification that is instrumental to the protection of non-financial data. 
  • PCI DSS–PCI SSC: For the integrity of payment transactions, this certification is crucial. 
  • ISO/IEC 27018:2019: Securing PII starts with this certification. 
  • ISO 27017:2015: Ensuring that this certification is present is a great way to safeguard your cloud services. 
  • NIST Cybersecurity Framework: As a result of this certification, the data security risk is substantially low. 
  • US Privacy Shield: This certification is mainly aimed at EEA citizens. Their complaints can be seamlessly resolved. 
  • ISO 27001:2013: This particular certification has the purpose of maintaining the integrity of the information security management system. 

Enterprise
Data Governance: How to Implement an Effective Framework

Due to the various enterprise Data Governance challenges,
maintaining the safety, quality, and integrity of your data assets can be a
daunting task.

Therefore, employing these seven steps is important as your enterprise Data Governance policy can go a long way in strengthening your Data Governance efforts throughout your company. 

Step 1: Focus on the areas that require improvement
You may be tempted to deal with all the data issues together. But, a surefire way to maintain the integrity of your data is to target one or two assets that provide the maximum scope for data asset improvement. When you selectively weed out Data Governance issues, you will find that it provides you with a sound foundation for enabling Data Governance across the company. 

Step 2: Leverage the power of data to the fullest
Data needs to be readily accessible if it is to be appropriately governed. Using various integration technologies and Data Governance best practices, modern companies can make sure of this despite the data existing in diverse forms. 

Step 3: Make rules, roles, and responsibilities
Ensure that people who work with information in your company data are governed by an optimal process that safeguards data integrity. 

Step 4: Ensure that the available information is high-quality
For the effectiveness of a Data Governance undertaking, one of the crucial requirements is data integrity. You can use the following systematic approach: 

  • Profiling: This refers to comparing your data to a predefined metric so that you can gauge if it’s good or bad. 
  • Parsing and Standardizing: This refers to the process of validating and correcting the data in accordance with the industry and company standard. You mainly check for things like case standardization and name formats. 
  • Enrichment: The idea of enrichment is simple. You garner and enhance your existing data using new data, such as the data pertaining to geocode.
  • Monitoring: This step is important if you want Data Quality to be consistent. 

Step 5: Set up infrastructure that ensures total accountability
It is worth mentioning that unless people are held accountable, your asset quality cannot be ensured. For this, you need to assign “owners” for each of your assets and provide them with the right technology for its management because manual processes are prone to errors even if they are well-monitored. 

Step 6: Move to a master data-based culture at your enterprise.
Another useful technique to incorporate into your Data Governance program is the process of moving from a transaction data–based culture to a master data-based culture. With proper Master Data Management, companies can ensure much better Data Governance.

Step 7: Develop a feedback mechanism for the sake of process improvement. 
It is crucial to have a feedback mechanism built into the process to allow for the constant improvement of Data Governance initiatives.

For this final step, graphical, real-time Data Governance tools can enable the feedback and enhancement cycle. Doing this will give you a clear idea of how the Data Governance initiatives are working on your information assets to make sure it is running as per your desire.

Conclusion

Proper Data Governance strategy is crucial for a company to effectively handle data. But, at times, it can be difficult.

While each organization has its unique challenges, the framework
provided above can ensure the implantation of an efficient enterprise Data
Governance structure.

Source: https://www.dataversity.net/data-governance-for-enterprises-important-principles-for-value-generation/

AI

Google launches suite of AI-powered solutions for retailers

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Google today announced the launch of Product Discovery Solutions for Retail, a suite of services deigned to enhance retailers’ ecommerce capabilities and help them deliver personalized customer experiences. Product Discovery Solutions for Retail brings together AI algorithms and a search service, Cloud Search for Retail, that leverages Google Search technology to power retailers’ product-finding tools.

The pandemic and corresponding rise in online shopping threaten to push supply chains to the breaking point. Early in the COVID-19 crisis, Amazon was forced to restrict the amount of inventory suppliers could send to its warehouses. Ecommerce order volume has increased by 50% compared with 2019, and shipment times for products like furniture more than doubled in March. Moreover, overall U.S. digital sales have jumped by 30%, expediting the online shopping transition by as much as two years.

Product Discovery Solutions for Retail, which is generally available to all companies as of today, aims to address the challenges with AI and machine learning. To that end, it includes access to Google’s Recommendations AI, which uses machine learning to dynamically adapt to customer behavior and changes in variables like assortment, pricing, and special offers.

Recommendations AI, which launched in beta in July and is now generally available, ostensibly excels at handling recommendations in scenarios with long-tail products and cold-start users and items. Thanks to “context-hungry” deep learning models developed in partnership with Google Brain and Google Research, it’s able to draw insights across tens of millions of items and constantly iterate on those insights in real time.

From a graphical interface, businesses using Recommendations AI can integrate, configure, monitor, and launch recommendations while connecting data by using existing integrations with Merchant Center, Google Tag Manager, Google Analytics 360, Cloud Storage, and BigQuery. Recommendations AI can incorporate unstructured metadata like product name, description, category, images, product longevity, and more while customizing recommendations to deliver desired outcomes, such as engagement, revenue, or conversions. And it lets Google Cloud customers apply rules to fine-tune what shoppers see and diversify which products are shown, filtering by product availability and custom tags.

Product Discovery Solutions for Retail also includes access to Google’s Vision API Product Search, which allows shoppers to search for products with an image and receive a ranked list of visually and semantically similar items. Google says Vision Product Search taps machine learning-powered object recognition and lookup to provide real-time results of similar, or complementary, items from retailers’ product catalog.

Beyond Recommendations AI and Vision API Product Search, Product Discovery Solutions for Retail ships with Cloud Search for Retail. Cloud Search for Retail, which is currently in private preview, pulls from Google’s understanding of user intent and context to provide retail product search functionality that can be embedded into websites and mobile apps.

“As the shift to online continues, smarter and more personalized shopping experiences will be even more critical for retailers to rise above their competition,” Google Cloud retail and consumer VP Carrie Tharp said in a statement. “Retailers are in dire need of agile operating models powered by cloud infrastructure and technologies like artificial intelligence and machine learning (AI/ML) to meet today’s industry demands. We’re proud to partner with retailers around the world and bring forward our Product Discovery offerings to help them succeed.”

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Source: https://venturebeat.com/2021/01/19/google-launches-suite-of-ai-powered-solutions-for-retailers/

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Trovata.io raises $20 million to aggregate enterprise bank accounts with AI

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Banking technology startup Trovata.io today announced it has raised $20 million in a series A round led by Wells Fargo Strategic Capital. The investment will be used to deliver new services and accelerate multi-bank APIs globally, the company says, and to add more bank distribution partners.

Trovata founder and CEO Brett Turner, who has spent time at Deloitte and Amazon, predicted that the rise of consumer bank aggregators driving fintech would lead to direct APIs for commercial banking and treasury services from banks globally. These prebuilt bank integrations, he believed, would remove enterprises’ need for legacy implementations or IT support and enable self-setup.

Turner launched 35-employee Trovata in 2019 in anticipation of the transformation, with a platform to aggregate companies’ bank balances and transactions natively on wholesale banking APIs. Using AI and machine learning, Trovata can automate cash-centric workflows such as cash reporting, analysis, and forecasting, allowing companies to see how much cash they have in real time while managing cash flow and building and maintaining forecasts.

Trovata acts as a high-performance data lake to store and manage bank data in a scalable multi-bank environment. The platform collects and normalizes data and then generates a forecast, leveraging machine learning to establish a baseline and analyze historical trends to increase forecast accuracy.

Trovata lets customers including Square tag data by region, entity, division, or arbitrary label. It also translates all non-USD denominated amounts into USD equivalents, offering the ability to drill down and generate forecasts for subsidiaries individually. A Google-like natural language search tool with a 300 millisecond response rate lets users find and tag key vendors, customers, and partners across millions of transactions.

“The tipping point is near, and … Trovata can play [a profound role] in wholesale banking and treasury services. The pandemic has spurned the need for better cash visibility, bank data in real time, and more proactive cash forecasting. Companies growing and contracting are in need of these things which have only accelerated interest,” Turner said in a statement. “Revenue is confidential, but our average deal size is roughly $25,00 and we’ve grown from 0 to nearly 100 mid-market and small enterprise customers in 18 months. We’ll be announcing a new up-market product for the enterprise later this month and expect to grow 4 times to 5 times this year.”

“We are keen on how technology is reinventing the treasury function into a modern, insight-driven operation that helps our clients deliver on their business strategy,” Wells Fargo Strategic Capital managing director Basil Darwish added. “Trovata provides distinctive technology and a client-centric approach to automating treasury services, and we’re excited to support their ongoing growth with this investment.”

Capital One Ventures and Pivot Investment Partners also participated in Trovata’s series A announced today, as well as existing early investors J.P. Morgan and Fintop Capital. This brings the San Diego-based company’s total raised to over $30 million, following seed and venture rounds totaling $10.6 million.

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Source: https://venturebeat.com/2021/01/19/trovata-io-raises-20-million-to-aggregate-enterprise-bank-accounts-with-ai/

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OncoHost raises $8 million to develop AI that predicts cancer treatment responses

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OncoHost, a company developing AI technology to characterize, analyze, and predict patient response to treatment, today announced it raised $8 million. The company says the funds will be used to finance OncoHost’s ongoing clinical trials, open a U.S.-based affiliate, and prepare for the upcoming launch of the company’s AI-based host response profiling platform.

The death rate from cancer declined by 2.2% from 2016 to 2017, the largest single-year drop ever, according to the American Cancer Society. But there’s an upward trend in cancer incidence. People born since 1960 will have at least a one-in-two chance of getting cancer over their lifetime, according to a recent study. While some cancers, like breast cancer, have a five-year relative survival rate of nearly 100%, others are difficult to treat and have high rates of recurrence. For example, glioblastoma and ovarian cancer recurs in nearly all patients, regardless of treatment.

OncoHost is a clinical stage precision oncology company developing a platform for predicting responsiveness to cancer therapies. Yuval Shaked, a member of Technion’s department of cell biology and cancer research, cofounded OncoHost in 2017 based on his research demonstrating that cancer therapies induce a range of unintended effects in healthy tissue, some which can promote tumor aggressiveness.

Cancer treatment modalities including immunotherapy, chemotherapy, targeted drugs, radiation, and surgery can trigger a range of physiological reactions in patients, Shaked says. These effects include elevated levels of cytokines, a broad and loose category of small proteins important in cell signaling, as well as growth factors, enzymes, and chemokines (a subcategory of cytokines) in the blood. OncoHost’s technology aims to identify and characterize responses to treatments and discover new targets to overcome treatment resistance.

Applying AI to predict cancer treatment response isn’t a new idea. A recent study published in Clinical Cancer Research found that an algorithm trained on standard CT scans could anticipate tumor response in patients with advanced non-small cell lung cancer. And in June, researchers in France launched a new trial to monitor patients for immune-related markers, gut microbiota, pharmacokinetics, pharmacogenomics, pharmacogenetics, and multiple biological parameters using an AI system.

But OncoHost uniquely analyzes changes in blood to monitor the dynamics of biological processes in response to a given cancer therapy, and to identify potential drug targets. The company’s lab uses protein analysis technology to determine the levels of thousands of proteins associated with therapy resistance and tumor spread in a single plasma sample. By analyzing these levels, OncoHost says it’s able to monitor a patient’s response to treatment in real time.

OncoHost says it’s established a preclinical program to test the efficacy of novel drug combinations in tumor models in mice. Moreover, the company says it’s launching a clinical trial for glioblastoma patients undergoing standard-of-care therapy.

“This investment round supports our mission to better predict response to immunotherapy and identify personalized treatment options for cancer patients, as we continue to expand our collaboration with pharma on clinical trials and drug development,” Shaked said. “Proteomic analysis is allowing us to make great advances in personalized cancer care, and we are grateful to our investors for their support in the midst of this particularly challenging time of a global pandemic. The future of personalized cancer care is no longer a distant reality, but within our reach.”

OurCrowd led the series B investment in Binyamina, Israel-based OncoHost, joined by a group of family offices and private investors. The round follows a $1 million grant from the Israel-U.S. Binational Industrial Research and Development (BIRD) Foundation, which OncoHost received together with its life sciences partner RayBiotech.

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Source: https://venturebeat.com/2021/01/19/oncohost-raises-8-million-to-develop-ai-that-predicts-cancer-treatment-responses/

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Swimlane raises $40 million to automate cybersecurity operations

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Swimlane, a Denver-based security operations management software provider, today announced it raised $40 million. The funding will be put toward accelerating partnerships and alliances, expanding R&D, and enabling further global expansion, the company says.

According to Markets and Markets, the security orchestration, automation, and response (SOAR) segment is expected to reach $1.68 billion in value this year, driven by a rise in security breaches and incidents and the rapid deployment and development of cloud-based solutions. According to Risk Based Security, data breaches exposed 4.1 billion records in the first half of 2019. That’s perhaps why 68% of business leaders in a recent Accenture survey said they feel their cybersecurity risks are increasing.

Swimlane’s suite of SOAR tools can automate 80-90% of the incident response process, the company claims, with extensible automated workflows and playbooks that interface with organizations’ existing tools while addressing data compliance laws and regulations. Swimlane’s platform offers dashboards and reports that spotlight metrics of note, including remediation cases in progress, alert levels, and threat intelligence, and which track (and allow admins to execute) scenario-specific security tasks and actions.

With its Technology Alliance Program, Swimlane teams up with companies to develop active integration points with security products, and through its free community (SecOpsHub.com) and content-sharing (AppHub) sites, it encourages customers and non-customers to collaborate to solve problems with Swimlane’s out-of-the-box integrations and common scripting language.

Swimlane recently launched Swimlane Analyst Hub, a way to aggregate its developer tools and content for security analysts, including PowerShell-based digital forensics and incident response tools. And in April, the company acquired Syncurity, a security orchestration company based in Bethesda, Maryland that provided incident response and case management to customers across health care and managed services industries.

Swimlane claims to support hundreds of organizations directly and through managed service security providers, largely in sectors like energy, finance, retail, and government.

“Without a doubt, automation will be one of, if not the biggest change realized by security operations in a generation. Security automation is a multibillion dollar market, and while hundreds of organizations already benefit from its initial adoption, the broad application of intent-based automation to every aspect of security is still in front of us all,” newly appointed CEO James Brear, former head of VMWare acquisition target Veriflow, said in a statement. “Swimlane is acutely focused on improving the automation piece of today’s SOAR solutions.”

EIP led Swimlane’s funding round announced today. The capital brings the company’s total raised to date to over $80 million following a $23 million round in May 2019.

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Source: https://venturebeat.com/2021/01/19/swimlane-raises-40-million-to-automate-cybersecurity-operations/

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