6 Business Continuity Strategies to Implement Post COVID-19

The health crisis of COVID-19 impacted businesses, people, and communities in numerous ways, causing us to change our strategies and the way we live going forward. This means that businesses are adapting to an incredibly new business landscape that’s changing the way we will work for the foreseeable future. Organizations are challenged with reinventing strategies, enabling virtual teams with remote workspaces, and exploring what’s possible for creating new innovations. Here are some key strategies to implement to accelerate business continuity and transition to a new working world:

Establish Your Team Leaders

The greatest asset any organization has are its people. Choose the team members with proven reliability, organization skills, and strong leadership qualities, especially under pressure. Situations like COVID-19 can prove to be stressful, so it is wise to choose to your Business Continuity team with these things in mind. Some roles you might consider designating specifically for Business Continuity purposes are Executive Business Continuity Manager (overall Team Lead), Communication Lead, IT Lead, Human Resources, Facilities/Maintenance, and Operations/Logistics Lead. These roles can depend on your specific business needs and internal departmental breakdown. Once you’ve decided your key players, it’s time to evaluate the primary business processes that need to continue in case of business disruption.

Document & Identify Critical Processes

From internal human resources processes like payroll processing, retirement plan administration, healthcare benefits to business operations such as supply chain management, customer support, operational processes, each of these requires certain access to various technology and secure applications. It is important to know if these processes will still be able to be performed with the current systems architectures and IT tools in place. That leads us into the next strategy, where we connect each process with existing resources in place to determine if the business continuity plan being developed will need specific changes, updates, or additions.

Identify Key Technology and Tools

Performing a proper assessment of current tools and technologies to validate capability will reveal where there might be gaps that need to be filled. One key question to consider is “Will these tools and technologies we currently have in place work in the case of a future change in working environment?” The answer to this will help identify what potential technologies or tools that might be needed in order to continue seamlessly operating with minimal disruption. Need help strategizing? Learn more about how to leverage cloud technology to improve business operations and increase performance efficiencies here.

Consider Contingency Technology and Tools

Is your system architecture set up for a new working structure for virtual teams? Is your cloud strategy crystal clear and strong enough to handle changing needs in terms of scalability and operations? Is it ready in case of another change in the working environment or future disaster? For example, it might be necessary to set up virtual workspace situations for employees. As a preferred AWS partner, Idexcel can help implement AWS Workspaces solutions in your organization – enabling business continuity by providing users and partners with a highly secure, virtual Microsoft Windows or Linux desktop. This setup grants your team access to the documents, applications, and resources they need, anywhere, anytime, from any supported device. Learn more about how we can help do that here.

Build A Customer Communication Plan

Communication with your staff, clients, and partners is perhaps the most important element of these strategies. The more they hear from you, the better off you will be with establishing trust and reliability. When communicating, be sure to follow these 3 guidelines:

1. Timing is everything. Responding quickly is key to establishing trust, visibility, and proactivity. It’s critical to be timely with messaging and depending on that communication sent, to give proper response and planning time to the recipient.

2. Be clear, concise, authentic, and provide value. Keep your communications simple and to the point. Create messaging that provides value, help, and support during any business changes or possible disruptions. Another key tip: keep it positive and avoid the use of negative words to evoke a more positive feeling and reaction to the communication. The more authentic and personable the messaging, the more likely you are to receive a positive response and invoke a sense of comfortability.

3. Leverage all communication channels. Social Media is a great way to keep in touch with your audience. Employees, clients, and partners alike are all very active, especially on LinkedIn, given it’s a key point of communication and connection digitally among professionals. Keep up with email communication with your teams internally as well, checking in often and also checking in how it may have impacted them.

Set Your Organization Up for Innovation

With a Business Continuity plan in place and the team assembled, now might be the time to consider strategically planning for innovative solutions. Specific technologies can be implemented to ensure accelerated business continuity measures are in place to better set your business and teams up for success.

For example, many organizations are adopting Machine Learning solutions with RPA (Robotic Process Automation). Many websites are using chatbots for answering general FAQs asked by the customers, eliminating the need for personnel to respond, and enabling them to focus on other tasks. They can positively impact the customer’s experience and are an ideal tool for short-staffed employers, saving thousands of hours of productivity and cost.

If you need help strategizing and creating your business continuity plan, get in touch with us to get connected with an expert.

Is Machine Learning the Solution to Your Business Problem?

The term Machine Learning (ML) is defined as ‘giving computers the ability to learn without being explicitly programmed’ (this definition is attributed to Arthur Samuel)Another way to think of this is that the computer gains intelligence by identifying patterns and data sets on its own, improving output accuracy over time as more data sets are examined. Since ML can be a challenging solution to implement, we’ve put together some foundational steps to assess the feasibility of building an ML solution for your organization: 

1. Identify the problem TYPE 

Start by distinguishing between automation problems and learning problems. Machine learning can help automate your processes, but not all automation problems require learning.

Automation: Implementing automation without learning is appropriate when the problem is relatively straightforward. These are the kinds of tasks where you have a clear, predefined sequence of steps currently being executed by a human, but that could conceivably be transitioned to a machine.

Machine Learning: For the second type of problem, standard automation is not enough – it requires learning from data. Machine learning, at its core, is a set of statistical methods meant to find patterns of predictability in datasets. These methods are great at determining how certain features of the data are related to the outcomes you are interested in.

2. Determine if you have the right data

The data might come from you, or an external provider. In the latter case, make sure to ask enough questions to get a good feel for the data’s scope and whether it is likely to be a good fit for your problem. consider your ability to collect it, its source, the required format, where it is stored, but also the human factor. Both executives and employees involved in the process need to understand its value and why taking care of its quality is important. 

3. Evalute Data Quality and Current State

Is the data you have usable as-is, or does it require manual human manipulation before introducing into the learning environment? A solid dataset is one of the most important requirements for building a successful machine learning model. Machine learning models that make predictions to answer their questions usually need labeled training data. For example, a model built to learn how to determine borrower due dates to improve accurate reporting needs a starting point from which to build an accurate ML solution. Labeled training datasets can be tricky to obtain and often require creativity and human labor to create them manually before any ML can happen.

4. Assess Your Resources

Do you have the right resources to maintain your ML solution? Once you have an appropriate question and a rich training dataset in hand, you’ll need people with experience in data science to create your models. Lots of work goes into figuring out the best combination of features, algorithms, and success metrics needed to make an accurate model. This can be time-consuming and requires consistent maintenance over time.

5. Confirm Feasibility of ML Project

With the four previous steps for assessing whether or not ML is right for your organization, consider the responses. Is the question appropriate for building an ML business solution? Is the data available, or at least attainable? Does the data need hours of human labor? Do you have the right skilled team members to carry out the project? And finally, is it worth it – meaning, will the solution have a large impact, financially and socially? 

It’s important to consider these key questions when assessing whether or not Machine Learning is the right solution for your organization’s needs. Connect with our ML experts today to schedule your free assessment. 

How to Minimize Your Cloud Security Risks

Minimize Your Cloud Security Risks

State of the Cloud Survey in 2018 revealed that 95% of respondents use the cloud for data storage purposes, with the number of businesses incorporating the technology increasing every day. In presence of such rapid growth, the possibility of cloud security risks also rises. Malware can penetrate your system and affect your system, allowing it to enter the cloud.

Overcoming these threats requires swift strategizing, adequate management of your operations and a well-planned execution. But how can this be achieved without sacrificing other technical elements of the cloud, such as the flexibility it offers your processes? Here are some strategies that will help you minimize your cloud security risk without hampering the pace at which you conduct your business.

1. Train Employees
The most common source of security threat in an organization happens to be the lack of awareness among employees. They lack security-related education necessary for battling such threats. A solid starting point is to hire a professional trainer who will teach your team how to develop and deploy security strategies, how to update your system’s security measures in time, while also demonstrating defense measures against threats.

Since security is the responsibility of every employee, try to involve the entire workforce of the organization into these training sessions. Keep your team updated with response sheets that will test their promptness and adaptability for a security threat scenario. It would also help to run unannounced security drills as this will keep your workers on their toes.

2. Build a Reliable Data Backup Plan
As we rely more on cloud computing, more data is being transferred in and out of the servers. This means that there is a higher chance of data being corrupted or misused. If your data is not backed up in time, you might end up with corrupt files that would compromise your operations. Make sure that you have a secure backup plan ready in case a mishap occurs. Additionally, distributing data and application across multiple locations will further help your offsite storage and disaster recovery needs.

3. Monitor Data Access
Backing up data is not enough to ensure its sanctity remains protected—limiting its access to only certain employees improves the stability of this data considerably. In other words, the smaller number of hands that touch the data, the better. It also becomes very easy to track down the source of a data breach when portals to access the data are limited and targeted.

This means that, although it is necessary to grant access to some workers, it would not be wise to give them access permanently. In such cases, your IT managers can take command, monitoring the access of data by establishing access controls. This also reduces the number of access codes, which would limit them to only one sign-on (SSO) authentication.

4. Encrypt the Data
So far, we have learned how to store and access data to minimize your cloud security risks. But, the access to such data should never be independent of encryption. No matter how small the data is, it needs to be protected cryptographically. It might seem unnecessary at times, but remember that there is always the possibility of data breach. If the data is encrypted, you will not be anxious of the possibility of improper handling or unauthorized access of the data midway. In short, your data will always be in safe hands.

5. Pilot Scenarios
Once ready with necessary arrangements for cloud data security, never forget to put it to the test. Devise scenarios where you test whether or not the system you have created can be trespassed or tampered with. The best path forward is to hire someone who has not been in the process of system development because they will not be familiar with any developmental codes. All in all, piloting helps in preventing cloud security threats instead of rectifying them. This move also saves time.

Once you apply the strategies outlined above, you will see a newfound fluidity in your workflow—the purer the data, the quicker the response time. Your decisions will be informed, and you won’t have to worry about any data leak in the course of the data transfer process. By taking these steps towards minimizing cloud security risks, you will be able to secure the integrity of your data with a stronger foundation.

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Thinking about DevOps culture? Inculcate these 5 must haves to make the most of it

5 Ways Data Analytics Can Help Drive Sales For Your Business

Digitalization has reached a new level, especially as the demand for better experiences has increased. In order to develop and deploy their software production, brands are constantly making use of DevOps to streamline their production cycles and make them more robust.

Let’s discuss what DevOps is all about, and how accepting the DevOps culture can help an organization achieve its goals in the best possible manner.

What is DevOps?

DevOps can’t be classified as a “thing” per se—it’s a set of principles that are necessary for laying the foundation of the developer culture. When one talks about objectives, the first thing that comes to mind is that DevOps is a methodology that is used to speed up the time to market, and further apply incremental improvements within the software space.

DevOps is a set of principles geared towards cross-training a multitude of teams involved in software development and infrastructure operations. In other words, it is a domain responsible for the design, deployment and maintenance of continuous integration and continuous delivery, or CI/CD frameworks.

The best way to integrate the two streams is by involving the development team and the operations team. This way, a channel of communication is opened, fostering a partnership between the two teams. In order to facilitate such communication channels, here is a list of key principles that need to be incorporated to find success in the DevOps space.

Foster a Collaborative Environment

The first and most important principle behind DevOps is the successful collaboration between the operations and the development teams. By creating a unilateral team, the DevOps team can focus on delivering the organization’s common goals and achieve its purpose. The fundamental idea behind this concept is to ensure both the team work together and communicate with each other so that they can share ideas and solve problems together.

This way, one can break down silos and align their people, processes, and technology towards achieving organizational goals. By aligning processes, such specialized teams can create a fluid experience, which can facilitate a culture of developments and deployments across the whole organization.

Create a culture to sustain end-to-end responsibility

Traditionally, developers and operations worked separately, within limited to no interaction between the different teams. However, in the DevOps environment, both teams are forced to work together to achieve the common goals and work as a centralized team. Nevertheless, with the shift in the DevOps culture, there is a different approach to this concept. Inefficiencies are addressed, plus there is a place for everything and everyone within the teams.

Facilitate continuous improvement

With added end-to-end responsibility, there is an additional need to adapt to the changing circumstances in order to evolve with the emergence of new technology, customer needs and any shifts in legislation.

DevOps focuses on continuous improvement, which is aimed at optimizing performance, speed, and cost of delivery.

Automate everything possible

Automation has become the need of the hour. As there is a growing awareness amongst consumers, continuous improvement is needed to meet customer demands. Keeping this thought in mind, there have been notable developments with respect to adopting tools that support automaton, as well as in streamlining the processes which include configuration management, the CI/CD pipeline, and more.

Throughout the automation process, several processes can be automated to perform efficiently with the given resources. This would include infrastructure provisioning, building new systems, software development and deployment, as well as conducting various tests that pertain to functionality and security compliance.

Through DevOps, teams can develop their own automated processes aimed at reducing the development and deployment turnaround time. Machines can be trained efficiently to deploy software quicker than their usual turnaround time, while keeping their reliability intact.

Focusing on customer’s needs
Brands that don’t innovate continuously can’t survive their customer’s demands. For this very reason, brands need to act like a lean startup, which need to innovate as the time changes. DevOps teams must always be on the move to meet their customer’s requirements. The data gathered from the automated processes can be analyzed to check if performance targets are met, which would ultimately delivery customer satisfaction.

Schedule Your Free DevOps Assessment

Big Data and Cloud Computing – Challenges and Opportunities

Two of the biggest IT trends making the rounds in today’s day and age include cloud computing and Big Data. Have you wondered what happens when you combine the two? Something positive may come out of it, while other times, not so much. Let’s explore and see what opportunities and challenges the union of these two worlds present to the users.

Big Data and Cloud Computing: Bridging the Gap

Big Data and cloud computing each have their own set of rules and specifications. However, when used together, they are capable of simplifying large datasets, offering value to businesses in a variety of industries and of different sizes. Big Data and cloud computing each offer their own set advantages while also including their own inherited challenges. Nevertheless, both technologies are still in the process of evolution, which is a double-edged sword in each case.

Regardless, there are plenty of companies using the two technologies in tandem to bolster how they operate. The combination of the two offers a number of benefits as they can increase a business’ revenue, while also reducing costs effectively. The cloud is all about providing infrastructure, and Big Data offers data solutions. Together, they can provide organizations with an advantage over the competition.

Advantages of Using Big Data and Cloud Computing Together

Agility: Lugging around heavy servers to store data is no longer feasible in the present. In other words, if one has to set up a traditional, physical infrastructure, chances are you will end up burning a hole in your pocket. With cloud computing, setting up the infrastructure is easy, convenient, and more hands-off. Cloud computing caters to businesses’ growing need for data management, which makes it easier for companies to optimize how they utilize their resources.

Elasticity: What is one of the best features of cloud computing? The ability to scale servers up and down as data needs change has to be one of them. Data is a volatile variable as it can look completely different from one minute to another. A good service provider needs to be able to accommodate these storage changes at a moment’s notice. This way, an organization can alter their storage space as their data needs change.

Data processing and efficiency: Data can be structured or unstructured, depending on the source it stems from. Data from social media is usually unstructured. Such data needs an efficient system to process it and to derive meaningful insights from it. Cloud computing can be seen as the answer to such problems. When Big Data is used to amass information from social media, cloud computing can be used to efficiently process this data and unearth meaningful insights that can adequately address a business’s needs.

Cost reduction: Cloud computing is a great solution for enterprises looking to make use of their technological tools, especially when on a limited budget. Big However, data platforms can be a little expensive, especially when it comes to managing such data. However, cloud computing makes it so customers need to pay only for the resources they use, with no upfront capital expenditure. It’s also worth considering that as a business’ servers scale up or down based on its data needs, it will only have to pay for the storage space it uses thanks to the cloud.

Simplifying complexities: Cloud computing is well known for its ability to automate business operations. With the various components available to users, there is no shortage of options for organizations looking to reduce the complexity of their operations, while also automating repetitive tasks.

Challenges of Using Big Data and Cloud Computing Together

Security concerns: There are security concerns to consider with the union of these technologies, as organizations begin to wonder how to safeguard sensitive, customer information against hackers and fraudsters. Addressing the cloud security risks inherent in cloud computing requires a shared responsibility model. Cloud service providers are responsible for the security of the cloud, while customers are responsible for Security in the cloud.

Conclusion
The cloud has become the go-to option for organizations looking to beat the competition and benefit from the immense technological advancements the cloud provides. Once such advancements are successfully mastered, there is a wonderful opportunity for organizations to reduce costs, use their technologies tools, and manage their data. The end result is often is organizations are able to meet their business goals, making the combination of the cloud and Big Data an ideal one.

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5 Ways Data Analytics Can Help Drive Sales For Your Business

5 Ways Data Analytics Can Help Drive Sales For Your Business

Companies that concentrate on their customers can attain incredible sales figures for several reasons. The current generation of consumers is not impressed with traditional advertising strategies. Instead, they want to feel valued as an individual, meaning they want the company to anticipate the customers’ needs in a personalized an accurate manner. And this level of personalization is something that data analytics can help a company achieve.

As customers’ demands grow, so should your product line. By predicting your customers’ needs, you can get ahead of the game, ensuring that your customer conversion and retention rates remain high. Having said this, it’s necessary to study your competition and how other players in your industry are using customer data to their advantage. If data is not used in a targeted and actionable manner, there’s a good chance your brand will lose traction in your space, paving the way for the competition to increase their share of the market at the expense of yours.

How can data analytics drive sales for your company?

1) Enabling segmentation: Segmentation is the key to building a well-catered product line. One size does not fit all in business these days; if you are trying to sell your products to every population segment, your marketing strategy will likely fail to hit the mark with some customers. A product’s popularity varies based on multiple factors, including buying habits, age, sex, product usage, etc. Through effective segmentation, you can personalize your marketing strategy according to the needs of a specific customer segment. This technique can help you go a long way in deciding what product is best-suited for which customer group, expanding your customer base and increasing your company’s sales figures.

2) Product development: Customization is the key to a good sales strategy. In order to stay ahead of the competition, companies need to compile and analyze data about their customers. Examining customer feedback is an effective way to determine how to sell your product as you can use this information to inform your marketing strategy. Using this information allows companies to work out any inefficiencies in their sales strategies, ensuring they emerge victorious in the competition. For example, companies such as Netflix and Amazon look at the viewer response rate for their shows and use this data to decide which shows to highlight on the homepages of their streaming services.

3) Help customers decide what they want: The best thing about data analytics is its power to make accurate predictions. By using strong predictive algorithms, companies can forecast what customers might want in the future. Not only is this an integral part of the sales prediction model, but it can also go a long way in helping retain the right customers. Customers might want to order the products being showcased and continue doing business with a company if the products they suggested appeal to them, which ultimately bolsters a company’s revenue and profitability. This approach helps companies retain customers as they are constantly reminded of products they may find appealing.

4) Pricing the products right: Pricing plays an integral role in helping companies put their products forward to the customers. Some industries can be extremely competitive, which means that a company can fail if it does not have the right data to inform them what the right prices are for each product. Through the use of data-driven pricing strategies, the right price can be easily unearthed by analyzing the competition, making it possible for companies to set the right prices on the right products. Pricing decisions can also be influenced based on customer spending patterns, allowing the company’s sales teams to ensure a price that is both profitable and affordable is set to a product.

5) Email campaigns: There is no denying that emails are one of the best ways to reach your customers. Having said this, one could also say that analytics can be used to capture which email subject phrases and words are most likely to capture the interest of the customers. This can only be achieved through the use of analytics. Understanding response rates, as well as the best days and times to send emails are useful ways to determine how to advertise products or services to customers via email campaigns.

Analytics can help businesses understand their data, while also helping them conduct a deeper dive into the ways and means in which this data can be used to enhance business operations. The more a company understand the data, the more effectively it can be used. Due to this very reason, it is important for businesses to use the right tools and unearth the right insights in order to inform their decision-making. When done right, this leads to higher customer retention rates and a higher profit margin.

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Best Practices for Ensuring DevOps Security

Best Practices for Ensuring DevOps Security

Best practices for ensuring DevOps security

It is often the case that there is no intersection between security modules and DevOps in a manner that is convenient. Naturally, security is an integral part of an organization, but the way we introduce its tenets at every crucial part of the DevOps process has been difficult to achieve since its inception. Usually, due to a general lack of expertise in the matter, the implementation of security becomes unbalanced, which hampers the speed and agility of the environment. The solution lies in partnering with the right team to lay out the security measures intelligently. Here’s how you can achieve this:

1. Implement latest policies
Your governance policies must be updated throughout the evolution of your company. While most codes of conduct remain omnipresent and intact within every company, some behavior control is specific to each company’s unique set of IT protocols. These codes of conduct must be properly followed throughout the entire pipeline to ensure there is zero leakage of data. Creating a transparent governance system also provides the engineers with the opportunity to openly share their concerns over anything that may seem fishy within the company. Many people overlook this aspect of security for being non-technical and moralistic, but enforcing and fostering such an environment in DevOps leads to long-term benefits.

2. Integrate DevSecOps
Optimally-secured DevOps requires collaboration from multiple paradigm internal functions to ensure that the security measures are implemented at all stages of the development cycle. Development, design, operation, delivery and support all require equal care and maintenance, and DevSecOps ensure you that you achieve this balance. DevSecOps is embedded throughout the DevOps workflow for balanced governance, and it renders cybersecurity functions such as IAM, privilege management, unified threat management, code review, configuration review and vulnerability testing. In such an environment where security is properly aligned with DevOps, you are able to attain a higher profit margin while minimizing costly recalls and post-release fixes.

3. Ensure vulnerability management
Systems should be thoroughly scanned and assessed to ensure there is security adherence at developmental and integration levels in a DevOps environment. The task of such an assessment is to inform the team of all the possible loopholes in the processes before production begins. Penetration testing is a great tool that helps track down weaknesses at these levels so that a prompt response can patch these issues.

4. Implement Automation
Human intervention increases the chances of errors in intricate tasks such as IAM, privilege management, unified threat management, code review, configuration review and vulnerability testing. It is best that you automate these processes in order to get more time to run security tests on your already refined product, while also minimizing system downtime and reducing vulnerabilities. Automating security protocols helps by not only increasing the speed of your testing and management, but also by improving your profits significantly.

5. Perform device testing
We often forget that the machine on which systems are working also need to be constantly checked for their performance, both in terms of efficiency and security. You cannot perform securely even if you have a software with top-tier security features if the machine on which it is loaded is malfunctioning. Ensure that these devices throughout the entire DevOps cycle are constantly being validated in accordance with your security policies.

6. Segment the networks
A continuous network flow might keep things easy and straightforward, but going this route will also make it easier for cybercriminals to access your servers. This problem is easily addressed by ensuring there is limited access on your application resource server. You can segment the networks so that no one error is spread throughout the DevOps environment, while also ensuring that no hacker has full access to all the data spread on the network.

7. Improve privileged access management
Admin controls provide a window in taking control of the data. The higher number of people have control over it, the more anarchy there is at handling the systems. Therefore, in an agile DevOps environment, try to minimize administrative privileges on various machines wherever possible because the more accessed a data point is, the more prone it is to security threats. Instead, you can store private and sensitive data on only a few local machines because apart from improving your security, doing so also makes it easier to manage. From this point on, you can monitor the legitimacy of your security in the aforementioned environment.

Conclusion
When paired smartly, Security and DevOps culminate in a productive intersystem. The tenets for reducing errors includes the identification of errors and scope of errors, limiting access to the network, ensuring there is minimal access, as well vulnerability management. The focus in DevOps must be more on the prevention of error rather than rectification of it. The tips outlined above help you achieve exactly that.

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Six Secrets to Big Data Success

Six Secrets to Big Data Success

Big Data has played a role in helping a number of industries immeasurably, as its role in the business world is becoming more important with each passing day. However, even though the utility of Big Data in a professional setting is immense, there are very few organizations capable to utilizing the technology at an optimal level to boost their operations.

A large number of companies fear that they will make mistakes with the technology, which stops them from moving forward with Big Data analytics and maximizing its value. This is because, when used poorly, Big Data analytics can make false predictions for the future. However, when implemented correctly, Big Data offers a lot of upside to an organization. Combine its capabilities with a focused vision and a competent team, and there is a good chance the technology will bolster your company’s operations and profitability.

Keep reading as we will help you develop such vision with our insights on how you can be successful with Big Data.

1. Skills matter more than technology

It’s no secret that without the right technological tools, it is nearly impossible to succeed in a growingly competitive and sophisticated business world. Nevertheless, technology alone is not enough to help you attain this success—having the skills to operate the technology properly is also needed. While talking of Big Data, your team’s skills are far more important than the technology itself since technical ability has a very small role to play in Big Data analytics. The Big Data analyst must have know how to come up with right business questions, developing a clear forward path to make the best of the technology. The analyst must also be competent enough to parse and analyze the unstructured data through pattern recognition and hypothesis formation. Eventually, the analyst should know how to use the appropriate statistical tools to generate a predictive analysis. It is not necessary for the analyst to have all these qualities before joining the organization. Instead, the organization must conduct workshops every now and then to update analysts on the latest uses of Big Data to add value to your business.

2. Run necessary pilots

Big Data Is generally adopted by firms that want a predictive analysis of market trends that they can use to to plan for their future. Such predictions are not always unearthed in a manner that ends up being useful to your organization. If the predictive data cannot be applied to your business, Big Data will not yield the fruits of success that you seek. Therefore, it is highly advisable that when looking for data-based predictions, you should run a pilot to determine whether your predictions can be applied to improve your systems or not. Doing so will not only help you rectify your errors, but will also help you redefine your prediction in a manner that better suits your market needs. Furthermore, running a pilot will also reveal any weak points on your plans from their inception through the execution of them. Thus, one pilot will strengthen the quality of your operations, as well as the overall strategies of your business.

3. Formulate targeted analysis

It is imperative that the data you compile from the market is raw and unstructured. The amount of data available is expected to grow eightfold over the next five years, according to Gartner, most of which will be unstructured. Keeping this in mind, organizations must ensure they are ready to parse and analyze the data in a manner that will be beneficial to your business. Targeted analysis is key as one dataset may be used to unearth insights about multiple topics, while other pieces of information may not need to be extracted as they may not be relevant to your goals. Know what you’re hoping to achieve before extracting insights from your datasets, and then proceed to analyze the data. Having the right technological tools beforehand that you can use to store and analyze data is key. Always keep a backlog with indices for relevant interpretations of the data, so that when you need to extract information from the same dataset in future, it will be readily available for any future analysis.

4. Extract the best data possible

Even a small dataset can sometimes prove to be effective in developing predictions, while it is also equally possible for big sets of unstructured data to lead you nowhere. Aim to always narrow the focus of the data you compile for analytical purpose without compromising the robustness of the predictions. Going this route will save you plenty of time, while also helping you attain an accurate and actionable prediction. Don’t continue running massive sets of unstructured data in the hope that it will definitely lead you to a robust prediction as this is a waste of your time.

5. Keep predictions within your organization’s operational ability

Do not aim for predictions that lie outside the ability of your firm. Not all organizations are equipped with the skills and technological prowess to make the most of your predictions, so make sure your predictions are targeted within your means. Most organizations have a limited amount of wiggle room and the challenge is to come up with predictions that your organization is comfortable with. Do not exhort unnecessary operational pressure on your organization because it will only hamper the pace and confidence of your workers.

6. Be adaptive

The best results in Big Data analytics are achieved when the most actionable predictions happen to be affordable for your firm. As discussed earlier, don’t place an unnecessary burden on your firm in the hopes of achieving the best prediction possible. Instead, bring adaptive changes to your firm slowly in a way that will help it accommodate the best of ideas. When these ideas match the capabilities of your firm, great results will be only an arm’s reach away.

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