Effective Integration Practices That Can Help Maximize the Potential of Mobile BI and Analytics

Mobile phones break through the traditional computing platforms as they help organizations to maximize their decision making potential irrespective of travel or location using Business intelligence (BI) and analytics. A smooth flow of data from the executive to operational level is possible when mobile devices infuse applications, services, and native functionality with computing, data discovery, communication, multimedia content, and transaction inputs. The integration allows for the refining customer and partner relations, employee yield, business operation, and sales and service in more than one innovative way.

BI and analytics are two tools that help innovate the data insights obtained from a mobile activity. While organizations initially fretted upon this idea for security breach and performance concerns, technological development has broken through these obstacles and paved the way for deploying secured applications on user devices. Instead of mobile applications being a replica of desktop-based BI reports, dashboards, and analytic capabilities, organizations are focused on engineering applications that enhance maximum user adoption to allow for better business’s operations, relationships and decisions.

Six of the crucial practices required for maximizing the potential of mobile BI and analytics are:

1. Development of Mobile BI and Analytics to expand business horizons

Businesses require easy access to data even when their personnel is out of the office because most business deals get negotiated right across the table. In order to improve the business’s operational efficiency, mobile BI and analytics allow for the access of customer information when dealing with business transactions on the go. A business person would be able to take decisions with aid from the ready access to data to ensure that opportunities for the business are never missed. By utilizing mobile applications that are developed with these capabilities, executives, managers, and frontline personnel can fundamentally change business interactions for the better. Moreover, the data collected on the field can be easily added on to the business’s data collection by allowing for write back capabilities in the mobile applications.

2. Adapting mobile applications into a collaborative environment

Since mobile phones have limited space for display of detailed visualization and data, the applications have to be designed to adapt the phone’s native functionality to generate the best BI and analytics solution. As the needs of the users grow, the applications need to accommodate easier navigation by using cloud computing platform in order to be able to access the information across mobile devices, desktops, and workstations. Only by using open application programming interfaces (APIs), BI and analytics can be smoothly integrated into the mobile environment to help focus on important elements such as key performance indicators (KPIs), real-time analytic trends, etc. Designers should also integrate other communicative applications such as email and social media to allow a business to access it clients at any point in time.

3. Analyze user experiences to improve satisfaction.

There is a lot of back work that has to be efficiently incorporated for making mobile BI and analytics successful. To start with expanding the adoption, performance, design, and relevance of mobile applications through every organization will help collect user’s data every time they tap on the screen or perform an action. Mobile devices supply geolocation data that can provide contextual insights into both performance issues and application use. A strategic monitoring and analyses of user experience can help understand and cater to each individual’s user experience both online and offline.

4. Use native device functionality to improve the user experience.

Compared to PCs and workstations, users are always excited about working with mobile which possesses native functionality such as touch gesturing, photography, integration with voice, video, and text communication, hands-free voice command capabilities, and integration with geolocation functionality. Designers should construct BI and analytics tools that can function in coordination with the mobile device’s inherent native functions such as OS interface, GPS, Push notification, offline applications, etc, to enable a simple access for any form of data. Breaking these boundaries will naturally help business to move beyond simple data or analytics consumption and build a two-way channel that can further their goals for data-informed decision making, smarter operations, and competitive advantages based on information innovation.

5. Secure an overall security strategy

Although mobiles have an inherent level of OS security, it is still a major concern whether it is part of an existing or new architecture. Businesses need to ensure that sensitive data is secure during data transfer between the applications and databases and the mobile server accessed by users, which can be situated behind the firewall. Procedures to deal with a lost or stolen device to ensure data is not compromised should be established. Further, the business’s identity, authentication, and access management processes should be securely set up so that functionality privileges and access permissions only go to select mobile users.

6. Move beyond analytics consumption to turn insights into action 

The addition of write-back functionality can be useful for creating data-driven mobile applications wherein users, be it the business or its customers, can input data from mobile devices into business applications such as ERP, CRM, OLTP, or other system-of-record application. This functionality will allow for personnel to respond to updated situations, and using the latest data, they could also create on-demand reports and visualizations that would aid the organization in determining correct strategies based on real-time views.

Final Word

Many organizations have barely scratched the surface with mobile BI and analytics. Yet their personnel are increasing their use of mobile devices, putting pressure on organizations to make faster progress toward enabling users to interact with data and apply insights for better business outcomes. With industry practices and technologies maturing, the time is right for organizations to develop mobile applications that further their goals for data-informed decision making, smarter operations, and competitive advantages based on information innovation.

How To De-clutter Your BI Dashboards To Discover Key Insights

Data, the primary key to everyday business opportunities, has gotten complex over the years due to technological challenges like data blending and data wrangling. The numerous complexities are a result of the scope and variety of big data and the integration of visualization and analytics tools. These glitches have not only slowed down data preparation but also affected the analytics stage. The effective use of data preparation tools can reverse the cycle of 80:20 ratios for data preparation and analytics into 20:80 proportions. In order to bring about this reversal, and accelerate the data preparation process, reduce waste and rework, and minimize complexity, data dashboards – the center of all data compilation and extraction- have to be effectively organized.

A data dashboard is the core information management system that helps to visually track, analyze and display the metrics of your business, key performance indicators, and other tandems that constitute the health of a business. These dashboards are customized according to the workflow process, and it is a superfluous system that connects all your data in the backdrop but presents it in the form of gauges and interpretable data. While the use of this system ensures real-time management of a business with integrated technology, most businesses fail to use them to their maximum potential because of inefficient structuring or organizing.

Three effective ways to prep your data dashboard for better productivity within a business are:

1. Streamline the right metrics: Most times, businesses just load up all their data and pick only the flashy appealing numbers and leave out the gruesome details. This is because the data entered is having too many metrics to filter out. The best strategy would be to set up only the crucial metrics for your business and streamline the rest of the data around these core metrics so that data inputs are the same but only better. Marketing data is the core of operations for an advertising company. If this company has finance at its core and marketing aligned as a subset, then the data generated will be drastically different from what is really required.

2. Don’t bog down with vanity metrics: A services related business does not require a crucial input from social media score, so, moving it to a different composition can ensure that sales are the prime focus and marketing is a secondary focus. All irrelevant metrics are crucial to a business and cannot be ignored, ensuring that all these dashboards are separately set up, monitored, and then integrated into the core reports can dramatically double up data efficiency.

3. Integrate into the open: Every department in a business is overprotective and keen on not opening their books to other departments. Just like a business needs open channels between all departments, the data on the dashboard should be open to the crucial analysts of each department and not restricted to a narrow channel at the end of the line. Allowing branching out ensures that some key factors can be interchangeably used by the unrelated department to identify crucial problems.

How Can you help?: While the above factors can benefit across the business, individual analysts too should help to de-clutter and effectively streamline data on the dashboard.The best ways to regulate the effective prep data for the dashboard would be to:

• Focus on what task you have been assigned so that too many people do not enter irrelevant feeds into the dashboard. Organize your team to handle each metrics individually, and streamline the dashboard.
• Don’t focus on dolling up a visually appealing end report. Set data as your priority on your dashboard and use the actual data to finish the end reports. This will ensure a solid output rather than just figurative numbers.
• Stay away from data that is beyond your scope. Feeding on data through automation tools can help avoid human errors.
• Do not dissect the data till you have integrated them across all metrics of the business. Instead of trying to break down data where and how you want, categorical logging will ensure the data is recorded at every required base and not lost in transition through channels.
• Follow the business’s categorization protocol, remove duplicate data, and scrub out both the dirty data (useless data) and outdated data.
• Lastly, don’t try to fit the data from the board into the wrong puzzle, sort it and match it. Also, ensure to check, revise, and update any misconnecting data that can churn out inefficient results.

The constant up gradation of algorithms will require the analysts and users of data dashboard to think on their feet and adapt clear-cut methods to align the dashboard to generate utility value. Every business has its own needs, but if they reorganize and simplify the collection of data, then there could be no hurdles to insightful analytics.

How Big Data Will Change Businesses In 2018

Market trends suggest that with an approximate growth of about $7.3 billion in 2018, the big data market size will be bound to break the $40 billion mark by the end of the year. The demanding growth in big data analytics has induced various industries to begin implementing and updating their big data systems to adapt to the higher workloads.

Structured and unstructured data has cracked the world of computational data and analytics into a divide. While algorithms and tools have enabled the easy categorization of structured data, unstructured data is left unsorted due to its complexity beyond the comprehension of simple tools. Unstructured data has been left out of most databases and wasted simply due to the sheer impossibility to classify or structure it into simpler forms.

Increased integration of business intelligence tools:

The implementation of machine learning, artificial intelligence (AI), and neural networks into the working processes of industries have begun to rapidly shrink the gap between structured and unstructured data. The intensive research in the fields of business intelligence is ensuring that all unstructured forms of data are analyzed, organized, scaled, and even used to predict trends which will not just generate viable data but also offer the required advantage for businesses to tap into unforeseen patterns to dramatically improve their key processes. Forrester has predicted that, with more than 70% of businesses integrating AI modules, businesses will have to be quicker and “think on their feet” to quickly tap into the upcoming trends and beat the competition.

The structuring of dark data:

Dark data that has constantly been discarded as unusable and left literally in the dark due to the unavailability of resources or appropriate tools will be streamlined into usable data with the use of these business intelligence tools. By processing and analyzing the old databases as well as that which will be acquired in the future, these business intelligence tools will help detect the often unaware or neglected quality anomalies. This enhancement will not just enable a correction in the business process but also augment the success of many businesses that have lost out on the competition.

Increased impact of IoT:

Further, Internet of Things (IoT), which has thus far proved to have a great impact on big data, will create a greater wave in the transfer of data through sensor technology. Many businesses are benefiting better by cashing in on the benefits of IoT enabled networks as compared to those businesses that are still hooked to outdated forms. An apparent benefactor of IoT would be retail businesses as they would be able to analyze their customer behaviors and other trends in real time through the data generated from their equipped smart stores. A simple sensor on a rack can help with real-time inventory management.

The greater shift from remote servers to cloud storage:

Another component that business will have to adapt to without fail for the success of the integration of these business intelligence tools would be cloud storage. These business intelligence components would cease to exist if businesses fail to utilize either or both cloud storage and cloud computing platforms to effectively collect, analyze or process any data. Accessibility to real-time data without the constraint of limited storage, like that of remote servers, is crucial not just for in-house data but also for the overall smooth management of every component of business intelligence tools.

Checking and updating security protocol:

Most importantly, or rather more obviously, another component that businesses cannot afford to lose out on is security protocol. With the extensive use of cloud technology, security risks are higher, and therefore require the constant upgradation of cutting-edge security measures to fight against cloud security threats. A simple breach could cause loss of sensitive data and repeated damaging attacks that could devastate the business. Business intelligence tools like AI have dedicated protective platforms that could avert a crisis even before occurrence that could otherwise be impossible for a human workforce to even control after a hack.

The need for big data and its smooth integration has been happening at a rapid pace in the past few years, and the current need of the hour is maximum utilization of these resources for a successful and disaster-free future for businesses. With a lot of businesses changing the current from the traditional to technological cores, the constant revision of algorithms is required to gain the edge over competitors. This year is all prepped for data-driven – innovation, discovery, and inventions.