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Data Availability 101: What Data Availability Means and How to Achieve It

What is data availability, and what does data availability mean for your business? Keep reading for an overview of data availability and best practices for achieving it.

What Is Data Availability?

Put simply, data availability refers to the ability of data to remain accessible at all times, including following unexpected disruptions.

You could think of data availability as the data equivalent of application uptime. Software vendors and service providers like to talk about how they guarantee an extremely high level of uptime (Amazon famously promises “eleven nines” of availability, for example) for their applications and services. They do this because they want to emphasize how much effort they put into keeping their software up and running even when unexpected events — like a disk failure, cyber-attack or natural disaster — occur.

Data availability is similar in that it is a measure of how long your data remains available and usable, regardless of which disruptions may be occurring to the infrastructure or software that hosts the data.

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Why Does Data Availability Matter?

Ensuring data availability is important for a number of reasons. Some of them are obvious, and some less so.

Most obviously, if you depend on data to power your business, you want to keep that data available so that your business can continue to operate normally. Lack of availability of a database that contains customer email addresses might prevent your marketing department from conducting an email campaign, for example. Or the failure of a database that hosts account information might disrupt your employees’ ability to log into the applications that they need to do their jobs.

Data availability matters beyond your own organization, too. In many cases, your relationships with partner companies depend in part on the sharing of data, and if the data you are supposed to provide is unavailable, it could harm your partnerships.

In some cases, licensing agreements with vendors or customers may also require you to maintain certain levels of data availability. So could compliance frameworks; for example, article 32 of the GDPR mandates that companies retain “the ability to restore the availability and access to personal data in a timely manner.”

Achieving High Data Availability

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Guaranteeing high rates of data availability requires addressing a number of factors that impact whether data is accessible:

The physical reliability of infrastructure

Are your servers and disks designed with data availability in mind? Is your data distributed across clusters so that it will remain available even if some parts of the infrastructure fail? Do you have tools and procedures in place to alert you to and help you resolve problems with the infrastructure? Are loads properly balanced across your infrastructure so that wear and tear is distributed evenly in order to maximize the longevity of the infrastructure as a whole? Are you prepared to handle disruptions like DDoS attacks, which could prevent access to your data?

Server and database recovery time

If your infrastructure does fail and you need to recover data, how quickly can you get your servers, disks and databases back up and running? The answer to this question depends not just on how quickly you can set up replacement hardware, but also how long it takes your software tools to perform tasks like rebooting operating systems and restarting database services.

Repair of corrupted data

Data can become unavailable not only when the infrastructure hosting it disappears, but also when the data becomes corrupted, and therefore unusable. How effective are your tools and processes at finding and repairing corrupted data?

Data formatting and transformation

Data that is not available in the correct format, or that takes a long time to transform from one format to another in order to become usable, can also cause data availability problems. Do you have the tools and processes in place to streamline data formatting and transformation?

Data Availability and Disaster Recovery

In most cases, data availability should be one component of your business’s disaster recovery and business continuity plan. Disaster recovery and business continuity involve making sure that all of your infrastructure, applications, and data are protected against unexpected disruptions.

When forming a disaster recovery plan, you should take into account the factors described above that impact data availability. You should also calculate metrics like Recover Time Objective (RTO), which measures how quickly you need to restore data in order to maintain business continuity, and Recovery Point Objective (RPO), which measures how much data you can afford to lose permanently following a disaster without causing a critical business disruption.

Learn more about the latest in high availability in our on-demand webcast.

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Syncsort Blog

Health care bots are only as good as the data and doctors they learn from

 Health care bots are only as good as the data and doctors they learn from

The number of tech companies pursuing health care seems to have reached an all-time high: Google, Amazon, Apple, and IBM’s Watson all want to change health care using artificial intelligence. IBM has even rebranded its health offering as “Watson Health — Cognitive Healthcare Solutions.” Although technologies from these giants show great promise, the question of whether effective health care AI already exists or whether it is still a dream remains.

As a physician, I believe that in order to understand what is artificially intelligent in health care, you have to first define what it means to be intelligent in health care. Consider the Turing test, a point when a machine becomes indistinguishable from a human.

Joshua Batson, a writer for Wired magazine, has mused whether there is an alternative measurement to the Turing test, one where the machine doesn’t just seem like a person, but an intelligent person. Think of it this way: If you were to ask a random person about symptoms you experience, they’d likely reply “I have no idea. You should ask your doctor.” A bot supplying that response would certainly be indistinguishable from a human — but we expect a little more than that.

The challenge of health care AI

Health is hard, and that makes AI in health care especially hard. Interpretation, empathy, and knowledge all have unique challenges in health care AI.

To date, interpretation is where much of the technology investment has gone. Whether for touchscreen or voice recognition, natural language processing (NLP) has seen enormous investment including Amazon’s Comprehend, IBM’s Natural Language Understanding, and Google Cloud Natural Language. But even though there are plenty of health-specific interpretation challenges, interpretation challenges are really no greater in this particular sector than in other domains.

Similarly, while empathy needs to be particularly appropriate for the emotionally charged field of health care, bots are equally challenged trying to strike just the right tone for retail customer service, legal services, or childcare advice.

That leaves knowledge. The knowledge needed to be a successful conversational bot is where health care diverges greatly from other fields. We can divide that knowledge into two major categories: What do you know about the individual? And what do you know about medicine in general that will be most useful their individual case?

If a person is a diabetic and has high cholesterol, for example, then we know from existing data that the risks of having a heart attack are higher for that person and that aggressive blood sugar and diet control are effective in significantly lowering that risk. That combines with a general knowledge of medicine which says that multiple randomized controlled trials have found diabetics with uncontrolled blood sugars and high cholesterol to be twice as likely as others to have a cardiac event.

What is good enough?

There are two approaches to creating an algorithm that delivers a customized message. Humans can create it based on their domain knowledge, or computers can derive the algorithm based on patterns observed in data — i.e., machine learning. With a perfect profile and perfect domain knowledge, humans or machines could create the perfect algorithm. Combined with good interpretation and empathy you would have the ideal, artificially intelligent conversation. In other words, you’d have created the perfect doctor.

The problem comes when the profile or domain knowledge is less than perfect (which it always is), and then trying to determine when it is “good enough.”

The answer to “When is that knowledge good enough?” really comes down to the strength of your profile knowledge and the strength of your domain knowledge. While you can make up a shortfall in one with the other, inevitably, you’re left with something very human: a judgment call on when the profile and domain knowledge is sufficient.

Lucky for us, rich and structured health data is more prevalent than ever before, but making that data actionable takes a lot of informatics and computationally intensive processes that few companies are prepared for. As a result, many companies have turned to deriving that information through pattern analysis or machine learning. And where you have key gaps in your knowledge — like environmental data — you can simply ask the patient.

Companies looking for new “conversational AI” are filling these gaps in health care, beyond Alexa and Siri. Conversational AI can take our health care experience from a traditional, episodic one to a more insightful, collaborative, and continuous one. For example, conversational AI can build out consumer profiles from native clinical and consumer data to answer difficult questions very quickly, like “Is this person on heart medication?” or “Does this person have any medications that could complicate their condition?”

Not until recently has the technology been able to touch this in-depth and profile on-the-fly. It’s become that perfect doctor, knowing not only everything about your health history, but knowing how all of that connects to combinations of characteristics. Now, organizations are beginning to use that profile knowledge to derive engagement points to better characterize some of the “softer” attributes of an individual, like self-esteem, literacy, or other factors that will dictate their level of engagement.

Think about all of the knowledge that medical professionals have derived from centuries of research. In 2016 alone, Research America estimated, the U.S. spent $ 171.8 billion on medical research. But how do we capture all of that knowledge, and how could we use it in conversational systems? This lack of standardization is why we’ve developed so many rules-based or expert systems over the years.

It’s also why there’s a lot of new investment in deriving domain knowledge from large data sets. Google’s DeepMind partnership with the U.K.’s National Health Service is a great example. By combining their rich data on diagnoses, outcomes, medications, test results, and other information, Google’s DeepMind can use AI to derive patterns that will help it predict an individual’s outcome. But do we have to wait upon large, prospective data analyses to derive medical knowledge, or can we start with what we know today?

Putting data points to work

Expert-defined vs. machine-defined knowledge will have to be balanced in the near term. We must start with the structured data that is available, then ask what we don’t know so that we can derive additional knowledge from observed patterns. Domain knowledge should start with expert consensus in order to derive additional knowledge from observed patterns.

Knowing one particular data point about an individual can make the biggest difference in being able to read their situation. That’s when you’ll start getting questions that may make no sense whatsoever, but will make all the sense in the world to the machine. Imagine a conversation like this:

BOT: I noticed you were in Charlotte last week. By any chance, did you happen to eat at Larry’s Restaurant on 5th Street?

USER: Uh, yes, I did actually.

BOT: Well, that could explain your stomach problems. There has been a Salmonella outbreak reported from that location. I’ve ordered Amoxicillin and it should be to you shortly. Make sure to take it for the full 10 days. The drug Cipro is normally the first line therapy, but it would potentially interact badly with your Glyburide. I’ll check back in daily to see how you’re doing.

But while we wait for the detection of patterns by machines, the knowledge that is already out there should not be overlooked, even if it takes a lot of informatics and computations. I’d like to think the perfect AI doctor is just around the corner. But my guess is that those who take a “good enough” approach today will be the ones who get there first. After all, for so many people who don’t have access to adequate care today, and for all that we’re spending on health care, we don’t yet have a health care system that is “good enough.”

Dr. Phil Marshall is the cofounder and chief product officer at Conversa Health, a conversation platform for the health care sector.

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Big Data – VentureBeat

3 Ways to Prevent a Data Breach from Becoming an Ordeal

It’s easy to think of a data breach as a one-time event, putting the affected company at risk for a workday and causing residual headaches for maybe a week. But when IT systems aren’t regularly audited for security and layered stopgaps aren’t put in place to mitigate the damage, even significant multinational agencies like Equifax can remain vulnerable for months. How can you make sure you’re not caught sleeping at the wheel when the time comes to put your data security to action?

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1. Audit Early, Audit Often

According to a study by Syncsort, nearly two-thirds of companies in the study perform security audits on their systems. Yet digging deeper, they discovered that for those who perform audits, the most common schedule was annual (39%), and another 10% audit every 2 years or more. Considering how sophisticated cyber-criminals have become and how frequent security events like Equifax seem to happen, this is unacceptable. An outdated system or plan removes any challenge hackers may face. And when it can take up to a year for an organization to act on their outdated infrastructure, the consequences of that inaction could multiply exponentially.

2. Don’t Stop at One

The most secure physical structures don’t rely on one layer on integrity. Make sure the structural integrity of your less tangible data and technology stays strong with multiple layers of resilience. Your multi-faced approach should address the vulnerabilities and strengths of the following areas:

  • Port/IP Address
  • Exit Point
  • File Security
  • Field Security
  • Command Control
  • Object Authority

That’s right: the integrity of your data depends on all of these layers, with even one neglected layer potentially being the only open door malicious actors need to capture sensitive information.

3. Communication is Key

In the unfortunate event that your organization suffers a security breach, there’s no need to exacerbate the issue by hesitating to inform the public. Any security event will understandably test the public trust, but you could suffer even more PR damage by withholding significant news for any amount of time. Acting fast isn’t just for IT administrators. Executive staff, retained PR agencies and any other public-facing entities in your organizations must stay on the ball to deliver the “Who, What, Why, Where and When” people need to know.

Download our Whitepaper today and discover the causes and effects of data breaches.

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Expert Interview (Part 2): Elise Roy on Human Centered Design and Overcoming Challenges with Big Data

In case you missed Part 1, read here!

Recently, while Elise was working with NPR, they discussed the fact that episodes of NPR programs posted online did not provide captions. While these shows generally have an article associated with them or a transcript of the conversation, Elise pointed out that NPR might be filtering out a significant portion of the population who might have hearing loss but are still able to appreciate an audio-centered show. Or, those who were completely deaf who liked the pacing captions brought and a less cluttered visual experience.

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Because of their conversation, NPR has a better understanding of an entire market they might be missing out on.

Her way of problem-solving is catching on.

“A couple years ago I was telling people about human centered design, they had no idea what I was talking about,” Elise says. “But now they’re starting to recognize the value it provides businesses and starting to see how they can create more targeted responsive solutions.”

Big Data plays an important role in creating more customer-centric solutions. It allows organizations to better understand how to react to the human experience and build more personalized and customized experiences and identify patterns that otherwise might have been difficult to see.

Currently, one of the biggest struggles with integrating the perspective of people with disabilities is that there are such a wide variety of disabilities– it can be challenging to design with each one in mind.

Elise says Big Data can help overcome those challenges.

There are already products on the market that benefit individuals with disabilities that use the power of Big Data and the Internet of Things.

For instance, there are companies developing doorbell home security solutions that alert users to motion and allow them to monitor the door remotely– an ideal solution for individuals with mobility problems. Innovation like this and others including the Roomba or self-driving cars not only make it easier for people with disabilities to live independently but are also products that the general population enjoys as well.

In order to continue to bring innovations like these to market, it will be essential that Big Data be paired with human centered design methods.

“This is because big data can easily be influenced by bias,” Elise says. “For example, we could only collect certain kinds of data and be missing out on a key thing that would get uncovered through the human centered design process during the observation phase.”

Recently, Microsoft hired several experts in bias reduction in Artificial Intelligence when they recognized their AI applications were biased in the sense that they were designed around the beliefs of those who were designing them rather than the people who were going to experience their applications.

Moving forward, Elise believes there needs to be symbiosis between Big Data and the human aspect of design.

Elise’s consulting business is still in its infancy, but she’s excited about potential impact on innovation that of looking at innovation through the lens of the disabled offers for businesses.

“There’s a lot of people who have gotten back to me and said it’s really impacted how they’re thinking about things,” Elise says.

We also have a new eBook focused on Strategies for Improving Big Data Quality available for download. Take a look!

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Why You Should Already Have a Data Governance Strategy

Garbage in, garbage out. This motto has been true ever since punched cards and teletype terminals. Today’s sophisticated IT systems depend just as much on good quality data to bring value to their users, whether in accounting, production, or business intelligence. However, data doesn’t automatically format itself properly, any more than it proactively tells you where it’s hiding or how it should be used. No, data just is. If you want your business data to satisfy criteria of availability, usability, integrity, and security, you need a data governance strategy.

Data governance in general is an overarching strategy for organizations to ensure the data they use is clean, accurate, usable, and secure. Data stakeholders from business units, the compliance department, and IT are best positioned to lead data governance, although the matter is important enough to warrant CEO attention too. Some organizations go as far as appointing a Data Governance Officer to take overall charge. The high-level goal is to have consistent, reliable data sets to evaluate enterprise performance and make management decisions.

Ad-hoc approaches are likely to come back to haunt you. Data governance has to become systematic, as big data multiplies in type and volume, and users seek to answer more complex business questions. Typically, that means setting up standards and processes for acquiring and handling data, as well as procedures to make sure those processes are being followed. If you’re wondering whether it’s all worth it, the following five reasons may convince you.

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Reason 1: Ensure data availability

Even business intelligence (BI) systems won’t look very smart, if users cannot find the data needed to power them. In particular, self-service BI means that the data must be easy enough to locate and to use. After years of hearing about the sinfulness of organizational silos, it should be clear that even if individual departments “own” data, the governance of that data must be done in the same way across the organization. Authorization to use the data may be restricted, as in the case of sensitive customer data, but users should not ignore its existence, when it could help them in their work.

Availability is also a matter of having appropriate data that is easy enough to use. With a trend nowadays to store unstructured data from different sources in non-relational databases or data lakes, it can be difficult to know what kind of data is being acquired and how to process it. Data governance is therefore a matter of first setting up data capture to acquire what your enterprise and its different departments need, rather than everything under the sun. Governance then also ensures that data schemas are applied to organize data when it is stored, or that tools are available for users to process data, for example to run business analytics from non-relational (NoSQL) databases.

Reason 2: Ensure users are working with consistent data

When the CFO and the COO work from different sets of data and reach different conclusions about the same subjects, things are going to be difficult. The same is true at all other levels in an enterprise. Users must have access to consistent, reliable data, so that comparisons make sense and conclusions can be checked. This is already a good reason for making sure that data governance is driven across the organization, by a team of executives, managers, and data stewards with the knowledge and authority to make sure the same rules are followed by all.

Global data governance initiatives may also grow out of attempts to improve data quality at departmental levels, where individual systems and databases were not planned for information sharing. The data governance team must deal with such situations, for instance, by harmonizing departmental information resources. Increased consistency in data means fewer arguments at executive level, less doubt about the validity of data being analyzed, and higher confidence in decision making.

Reason 3: Determining which data to keep and which to delete

The risks of data hoarding are the same as those of physical hoarding. IT servers and storage units full of useless junk make it hard to locate any data of value or to do anything useful with it afterwards. Users use stale or irrelevant data as the basis for important business decisions, IT department expenses mushroom, and vulnerability to data breaches increases. The problem is unfortunately common. 33% of the data stored by organizations is simply ROT (redundant, obsolete, or trivial), according to the Veritas Data Genomics Index 2017 survey.

Yet things don’t have to be that way. Most data does not have to be kept for decades, “just in case.” As an example, retailing leader Walmart uses only the last four weeks’ transactional data for its daily merchandising analytics. It is part of good data governance strategy to carefully consider which data is important to the organization and which should be destroyed. Data governance also includes procedures for employees to make sure data is not unnecessarily duplicated, as well as policies for systematic data retirement (for instance, for archiving or destruction) according to age or other pertinent criteria.

Reason 4: Resolve analysis and reporting issues

An important dimension in data governance is the consistency across an organization of its metrics, as well as the data driving them. Without clearly recorded standards for metrics, people may use the same word, yet mean different things. Business analytics are a case in point, when analytics tools vary from one department to another. Self-service analytics or business intelligence can be a boon to an enterprise, but only if people interpret metrics and reports in a consistent way.

When reports lack clarification, the temptation is often to blame technology. The root cause, however, is often the mis-configuration of the tools and systems involved. It may even be in their faulty application, as in the case of reporting tools being wrongly applied to production databases, triggering problems in performance that mean that neither transactions nor analytics are satisfactorily accomplished. Ripping out and replacing fundamentally sound systems is not the solution. Instead, improved data governance brings more benefit, faster, and for far less cost.

Reason 5: Security and compliance with laws concerning data governance

Consequences for non-compliance with data regulations can be enormous, especially where private individuals’ information is concerned. A case in point, the European General Data Protection Regulation (GDPR) for May 2018 sets non-compliance fines up to some $ 22 million or four percent of the offender’s worldwide turnover, whichever is the higher, for data misuse or breach affecting European citizens.

Effective data governance helps an organization to avoid such issues, by defining how its data is to be acquired, stored, backed up, and secured against accidents, theft, or misuse. These definitions also include provision for audits and controls to ensure that the procedures are followed. Realistically, organizations will also conduct suitable awareness campaigns to makes sure that all employees working with confidential company, customer, or partner data understand the importance of data governance and its rules. Education and awareness campaigns will become increasingly important as user access to self-service solutions increases, as will the levels of data security already inherent in those solutions.


If you think about data as a strategic asset, the idea of governance becomes natural. Company finances must be kept in order with the necessary oversight and audits, workplace safety must be guaranteed and respect the relevant regulations, so why should data – often a key differentiator and a confidential commodity – be any different? As IT self-service and end-user empowerment grow, the importance of good data governance increases too. Business user autonomy in spotting trends and taking decisions can help an enterprise become more responsive and competitive, but not if it is founded on data anarchy.

Effective data governance is also a continuing process. Policy definition, review, adaptation, and audit, together with compliance reviews and quality control, are all regularly effected or repeated as a data governance life cycle. As such, data governance is never finished, because new sources, uses, and regulations about data are never finished either. For contexts such as business intelligence, especially in a self-service environment, good data governance helps users to use the right data in the right way, to generate business insights correctly and take sound business decisions.

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Blog – Sisense

Data Science and Visual Analytics for Operations in the Energy Sector

iStock 855386302 e1528773593698 Data Science and Visual Analytics for Operations in the Energy Sector

In recent years, Oil and Gas Companies have been challenged to adapt to lower crude prices. With the recent crude price increase, there has never been a better time for energy companies to transform their operations.

From upstream exploration and production to logistics and downstream refining, energy trading, and the portfolio investments; there are opportunities for optimization. All of these areas benefit from today’s advances in data science and visual analytics. The past few years many companies were forced to reduce costs or consolidate; it was a period of survival. Now, the successful companies of the future are digitizing smarter.

Driving business operations from analytic insights applies to many facets of the digital energy business including:

Modernized Grids and Smarter Oilfields

With TIBCO Systems of Insight:

  • Analysts can create self-service analytic apps to deliver insights into all aspects of a process, quality, and costs.
  • Data scientists can develop machine learning intelligence into sensors, processes, and equipment to reduce data bottlenecks and take action at the point of impact.  
  • Operations and IT developers can empower more users and scale complex, computationally intensive workloads in the cloud.

Asset Portfolio Value Optimization

Using Spotfire, analysts can invoke smart data wrangling, data science, and advanced geoanalytics to develop accurate valuation of assets and resource plays for optimal capital allocation. Spotfire community templates for decline curve analysis and geoanalytics enable these sophisticated calculations to run with point-click configuration, invoking Spotfire’s powerful inbuilt TIBCO Runtime R engine.

Predictive Maintenance, Process Control, and Process Optimization

Spotfire and TIBCO Statistica can readily analyze large amounts of data from internal and external IoT data sources. The combination of your industry expertise with TIBCO’s latest visual, predictive, and prescriptive analytics techniques enable you to address all of your process and equipment surveillance challenges.

Business Operations and Supply Chain Management

Provide managers, engineers, and business users self-service access to data, visualizations, ​and analytics for visibility across the entire value chain. Respond to evolving needs and deliver actionable insights that enable people and systems to make smarter decisions. Reduce time spent on compliance reporting and auditing.

Energy Trading

Develop insights faster and bring clarity to business issues in a way that gets all the traders, managers, and financial decision-makers on the same page quickly. For companies trading in multiple commodities, TIBCO Connected Intelligence can be deployed as a single analytics platform that brings a consolidated view of risks and positions, compliance, and results. Read more about it.

Learn More Firsthand

Listen to TIBCO’s Chief Analytics Officer Michael O’Connell explain how companies are leveraging the latest Spotfire innovations, optimizing exploration and production efforts and investments, and gaining a decisive advantage. And hear Stephen Boyd from Chevron present a real-world case study on TIBCO Connected Intelligence. Register now for the quarterly Houston area TIBCO Spotfire® User Group Meeting taking place on Thursday, June 14th, at the Hilton Garden Inn. Or find a Spotfire Meetup near you.

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The TIBCO Blog

CRMDialer President Dimitri Akhrin: Raw Data Is No. 1 Thing

Dimitri Akhrin is president of

In this exclusive interview, Akhrin addresses

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CRMDialer President Dmitri Akhrin

CRM Buyer: What are some of the current trends you see in the CRM space?

Dimitri Akhrin: AI is giving the ability to gain insight, and the most important example is visitor tracking. That’s a big component — knowing how in real time to react to something a prospect is doing on a site.

It’s going to allow prescriptive recommendations about what to do in a particular moment, instead of having to crunch data after the fact. The key is having a massive amount of data and being able to leverage the different data points.

CRM Buyer: What’s the key to making data useful?

Akhrin: Look at the best person that you have in an organization and focus on enabling everybody else to be like that person. You have your best sales rep who has worked with you for five years — someone who’s encountered all kinds or rejections and pushback.

For a new person coming in, scripts can be hard to navigate. But if I have internal raw data that my best sales rep has had, I can parse that data in real time through transcription services to analyze it to suggest the types of questions and answers to give.

This enables each sales person to be like the best sales person, using real data from past experience.

CRM Buyer: What is the best way to display these suggestions to sales reps? Should they be a script, or a bulleted list, or some other format?

Akhrin: The suggested thing for display is what the CRM can handle. If the way the CRM system works is on the scripting side, it can pop up as a script, or it can be a bulleted list of possible items to say in response to an objection.

CRM Buyer: What are the keys to improving sales using CRM?

Akhrin: CRM is the equivalent of a supply chain or assembly line. The CRM system is a process that an entire company’s business is built on from the beginning. In a successful company, all of the processes will go through a single system. As soon as an agent goes out of that system — they automatically lose efficiency.

If there are external tools that constantly have to be used, it keeps the employee from being as successful as possible. You need a single system in order to have a steady and moving pipeline.

CRM Buyer: Why is it important to integrate a CRM system with sales and marketing?

Akhrin: You need a single application or tool to let you know everything about a prospect or customer in real time, without having to be super tech-savvy or to access the information elsewhere.

A good CRM system allows you to have a conversation based on what’s relevant to each individual. At a coffee shop, for instance, you will have a better relationship with that brand if it remembers what coffee you prefer, and it doesn’t recommend tea.

CRM Buyer: What’s the key to making sense of all the data — and making it actionable?

Akhrin: Artificial intelligence is being able to analyze the past to make decisions in the present. It really comes down to having quality data about what has happened before, so you can build the model.

It’s important to have the proper information and to set up the rule engine correctly. Having the raw data is the No. 1 thing, and you build your decisions on top of that.

CRM Buyer: What’s in the future for CRM? How is it evolving and changing?

Akhrin: We’re going to see CRM becoming more and more integrated into a single system — phone, email, helpdesk and various applications. Right now, we’re seeing a big drive to not just have it be optional, but required, that there’s an open-facing public API, which allows other systems to connect and to push and pull data.

With this open exchange, people won’t have to switch screens and use multiple tabs to do their job on a daily basis. Once you have everything built on a single system, companies will be able to create smart rules about what the next action should be. The key is to have all those processes and interactions done in a single system.
end enn CRMDialer President Dimitri Akhrin: Raw Data Is No. 1 Thing

Vivian%20Wagner CRMDialer President Dimitri Akhrin: Raw Data Is No. 1 Thing
Vivian Wagner has been an ECT News Network reporter since 2008. Her main areas of focus are technology, business, CRM, e-commerce, privacy, security, arts, culture and diversity. She has extensive experience reporting on business and technology for a variety
of outlets, including The Atlantic, The Establishment and O, The Oprah Magazine. She holds a PhD in English with a specialty in modern American literature and culture. She received a first-place feature reporting award from the Ohio Society of Professional Journalists.
Email Vivian.

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Data Socialization 101: What Is Data Socialization, and Why Should You Care?

Data socialization is one of the newest buzzwords in the world of data analytics and management. What does data socialization mean, and what can it do for you? Find out in this post.

Data Socialization 101 What Is Data Socialization and Why Should You Care banner Data Socialization 101: What Is Data Socialization, and Why Should You Care?

What Is Data Socialization?

In a nutshell, data socialization refers to the sharing of data and data analytics tools with all members of an organization. The key idea behind data socialization is to make data-driven insights available to everyone in a self-service fashion.

Another way of defining data socialization is to say that it involves the “democratization” of data. Whereas the typical business has traditionally assigned data analytics tasks to only a handful of employees who specialize in data management, the data socialization concept aims to involve everyone in the organization in collecting, managing, analyzing and reacting to data.

Why Does Data Socialization Matter?

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Data socialization is innovative because it helps businesses to double down on their ability to leverage data.

These days, most businesses collect huge troves of information, ranging from machine data (like Web server logs) to manually entered customer reports and everything in between.

Yet traditionally, the extent to which businesses have leveraged that data has been limited. As noted above, the ability to access and analyze business-critical data has typically been available only to a small team of data specialists. Unless data analytics or data management is an explicit part of your job title, you probably didn’t do much with data; instead, you relied on other people — the ones who specialized in data management — to collect and analyze your business’s data for you, then provide recommendations to you based on it.

From a business standpoint, this approach is not ideal, for two main reasons:

  1. When a business relies on only a small group of data specialists to process all of its data, those specialists are likely to become overwhelmed. It’s difficult for a small group to process an entire business’s data single-handedly and deliver relevant insights and recommendations to every business unit. This is especially true today, when the amount of data that organizations collect is larger than ever.
  2. In most cases, data specialists have a limited understanding of other parts of the business. Their ability to leverage data in ways that benefit other business units is therefore limited, too.

Data socialization aims to solve these challenges by placing data and data analytics tools directly in the hands of the people who can use them as part of their jobs.

For example, if you work in marketing, data socialization means that you can collect and analyze data related to marketing campaigns yourself, rather than depending on data specialists to perform that task for you. Because you know your business’s marketing needs better than anyone who does not specialize in marketing, you are better positioned than the rest of your organization to derive relevant insights from that data.

Similarly, a customer service specialist can benefit from data socialization by being able to access and analyze information related to each of the customers he or she supports.

Data socialization does not mean, by the way, that data specialists have no role to play in data socialization. They remain the experts, and they oversee the tools and processes that enable other parts of the organization to perform data self-service. But they are no longer solely responsible for data management.

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Best Practices for Data Socialization

When you want to empower everyone in your organization with the ability to manage and interpret data, you need to approach data management somewhat differently than you would when only data specialists are involved in the process.

Most obviously, you need data management tools that enable self-service without requiring a great deal of expertise. This might seem difficult to achieve, but in fact, data integration and analytics are simpler today than they once were. Even your non-technical employees will likely be able to work with data much more effectively using modern data management tools than you might expect.

That said, the ability to deliver a streamlined data experience is important for enabling data socialization. By streamlined, I mean providing a data analytics process that is free of complex technical kinks. For example, you should not expect your non-data-specialist employees to be able to perform complex data transformation or data integration tasks. Nor should they be expected to clean up low quality data sets.

Instead, you need to provide them with data that is readily usable. Providing tools that enable them to visualize data easily is also important.


In today’s data-driven world, everyone in the business stands to benefit from being able to access and interpret data that is relevant to his or her role within the organization. By embracing data socialization, businesses can make data analytics more efficient and faster, reduce the burden they place on their data specialists and provide more relevant data-driven insights to employees who stand to gain the most from them.

Make sure to download our eBook, “The New Rules for Your Data Landscape“, and take a look at the rules that are transforming the relationship between business and IT.

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Solving Data Quality Problems Is Not (Only) Programmers’ Responsibility

Most software is of little use without data to feed into it. When the data is bad, the software performs poorly. Whose job is it to make sure that the data that applications use is of high quality? If you think the burden is on programmers alone, think again.

It may be tempting to assume that developers bear primary responsibility for ensuring that the software they write works properly no matter which data is fed into it. After all, since they write the code, they alone have the power to control how an application will respond when it receives low quality data.

In fact, however, the responsibility for ensuring that software works properly no matter which data is fed into it is not the job of programmers alone. Everyone in the organization should play a role in ensuring data quality, because the ability of programmers to address this issue is quite limited.

Let’s explore this topic in a bit more detail.

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Applications and Data Quality

Data quality can make or break applications. If an application receives data in an incorrect format, the information that an application tries to retrieve from a database is incomplete or another type of data quality issue occurs, the application often won’t be able to do its job.

Consider, for example, a website that looks up credential information in a database in order to authenticate users. If there are duplicate entries for the same username, the application might not let the user log in at all. Or maybe it will default to using the first entry to authenticate the user, which may or may not work. Either way, the application’s performance will be erratic and unpredictable at best.

A well-written application will include logic to handle data quality problems. In the example from the preceding paragraph, the application will ideally be “smart” enough to check whether duplicate entries exist in the database for the same username and react in an intelligent way in the event that a duplicate occurs. In that event, it might require the user to reset his or her account information, for example.

But the fact is that not all applications are this “smart.” If a data quality problem occurs that the application was not designed to anticipate and handle, something random might happen. It could spew cryptic error messages that confuse users. It may continually restart itself, only to have the data quality problem recur each time. It might freeze and stop responding entirely.

In any case, unless the application was designed to handle a specific data quality problem, something bad will probably happen whenever that data quality issue occurs.

Programmers’ Data Quality Responsibilities

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In a perfect world, programmers would be able to see into the future and anticipate all possible scenarios in which data quality problems could disrupt the ability of the software they write to operate properly. They would also have the time and skills to include code within the applications that can address those problems.

In the real world, of course, the amount of time and effort that programmers devote to handling potential data quality problems in their software is quite limited. They might — and should — include code to perform basic data validation, which ensures that data input into an application is complete, formatted as expected and so on. They might also take steps to validate data input for security reasons, in order to prevent “injection” attacks and the like.

Yet even the best programmers can’t foresee every type of data quality issue that could occur within their applications. And most of them don’t have the time to write code for handling those issues anyway. Plus, even if they did, their applications might end up being quite bloated by functions that handle obscure data quality issues.

So, while programmers should ensure that their applications perform basic data validation and data security checks, it is hardly realistic to expect programmers to address every potential data quality issue that could impact their applications.

Data Quality Is Everyone’s Job

This is another reason why data quality is the responsibility of everyone within your organization. Not only data engineers but any employee who interacts with data has a part to play in ensuring that the information that powers your business is free of data quality problems.

The fact is that no single individual or group can totally prevent data quality errors. Your data governance strategy can include steps to mitigate data quality errors, but it won’t be able to prevent errors entirely. Your data engineers can run tools to check for data quality problems within existing databases, but they will almost certainly overlook some issues. And your programmers can design applications to respond intelligently to data quality problems, but again, they can’t solve every type of data quality issue that their applications might encounter.

By making data quality everyone’s job, you maximize your organization’s ability to find and fix data quality issues before they impact business productivity. Perfect data quality is impossible in most cases, but when everyone takes responsibility for helping to ensure data quality, you can come close to perfection.

Download our eBook today and discover the new age of data quality.

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New White Paper! Leveraging the Potential and Power of Real-Time Data

Syncsort has released a new white paper titled, “Leveraging the Potential and Power of Real-Time Data.” Business leaders recognize that their companies, industries, and markets are being disrupted by nimble digital players that are leveraging real-time information to respond to customers, predict trends, and manage operations. To compete in this new environment, they need to engage with customers, partners, and employees with speed and agility, to understand what is happening in their environments as they change, and to be able to employ cognitive technologies to predict and get ahead of trends.

Leveraging the Potential and Power of Real Time Data banner New White Paper! Leveraging the Potential and Power of Real Time Data

Emerging real-time data solutions, including those based on open source projects such as Apache Spark, Apache Kafka and commercial offerings, many of which are supported in the cloud, are enabling just about any organization to be transformed into a real-time enterprise.

In this whitepaper and learn more about:

  • The challenges to delivering real-time data services
  • Business cases for real-time data analytics
  • Data analytics in action
  • Recommendations on competing in today’s fast-growing digital economy

Download the white paper today!

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