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 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.

Five Reasons Why Business Intelligence Implementation Can Fail

 While there are loads of advice on how to institute a business intelligence (BI) tool into an organization’s curriculum, there is hardly any foresight on how to turn the BI tool into a part of the organization’s culture for effective usage. Scientific facts state that a smartphone is a tool that people just flash for having a sense of belonging with the crowd and use it only for limited purposes, and thereby fail to expend the smartphone’s full capabilities. Similarly, BI tools are not effectively expended and thus lose their value or fade out over time. Consequently, the critical question is: is it the smartphone’s fault or the user’s?

Bill Hostmann, a vice president and renowned analyst at Gartner, stated that “Despite years of investing in BI, many IT organizations have difficulty connecting BI with the business, and to get business users fully involved and out of the ‘Excel culture’ “. Periodical surveys have indicated that the following five reasons are the root cause for the implementation of BI tools to fail:

1. Using The BI Tool As A Depository :- There has to be an efficient system of data management and the tradition of transcription culture among the employees should be imbibed in their system and the business support team should aid in the process of constant result generation. Nobody used computers when they were introduced, but today the whole world of business is progressively built around computers.
2. Not Allowing Free Flow Of Data :-  Often employees collect data from the BI tools and transcribe them into the excel sheets or other similar formats and leave it there. This leads to stagnation, thereby leading to withholding of analysis from being circulated around the company. When crucial data is unavailable, the BI tools become ineffective to produce competent results. Instead of just collecting data, employees should have the responsibility to feed their results back into the system and allow access to other users, failing which there would be a slow motion domino collapse of the data.
3. Plugging In A Third Man To Get The Answers :-  When organizations hit a rock wall, they give up and bring in third-party experts to take care of their business needs. This results in business decisions being formed from an outside-source rather than an in-house expert. Only employees that are part of the organization will be able to tackle and estimate the businesses’ problems because to an outsider it’s just meaningless numbers. The individual has to take the pick at the buffet table and not eat from another’s plate.
4. Play Against Strategy :-  Most employees just think about the BI tool installed as a one stop shop for answer, but they need to strategically equip themselves to use the tool to their advantage. This also means that the BI tool should be up to date with the market and quality checked to ensure that the data is not being lost in transaction. Having a combined team of IT experts and business analysts dedicated towards the BI tool will ensure quality results.
5. Failing To Train And Adapt :-  Employees believe that they are always right and that the software is beneath them. Having a positive approach towards change and trying out a handy BI tool only makes life easier. A BI tool only provides a wider berth of data to swim in, so adapting with technology only showcases the employees potential and does not undermine them for using a resource.

In the words of James Richardson, a research director at Gartner, “Business users must take a leadership role in the BI initiative — only with their full engagement will investment in BI ever realize its potential.”

Five ‘Must Haves’ in the Self-serving BI Tools

 

The world of business analytics has seen some major shifts in its analytical frame. The current demand for instant usability and conformability has made the traditional lengthy-process of getting out reports via business analyst or analysis-specific IT teams redundant. Even before the data reaches the actual business users for ultimate decision making, the time taken for the data travel and subsequent conversion causes it to expire upon arrival.

The rise of self-service business intelligence (BI) is indeed unfathomable. Several BI companies have established a strong foothold in BI and Gartner has also predicted that “self-service BI platforms will make up 80% of all enterprise reporting by 2020”. Self-service BI tools, as the term denotes, will not only eliminate the need for a mediator to transliterate the information into usable data but also help beat the time delay.

The major perk of a self-service BI tool is that, in comparison to a lot of BI data analytics tools in the market that require SQL developers or BI experts, a person of reasonable understanding would be able to use the tool’s dashboard to easily manipulate the data into the required track. Without any specialized training, the management, marketing, business development, or any controlling department within the business could easily access the businesses’ database to build the necessary reports to answer crucial business questions.

Here are the top 5 ‘must haves’ when you consider a self-service BI tool:

1.    One Stop Shop – The tool must be able to correlate data and not be a restricted user interface requiring multiple individuals to manually generate statistics and then have another one drive the final report. In short, one tool should be the one stop for all business needs.

2.    Easily Integrated – Tool should be easy to integrate into existing systems in order to be able to get it working without any delay or the requirement of a major system upgrade in the existing database. Adaptability is a priority.

3.    Real-time – Constant real-time update feasibility to ensure that the numbers are live and not require a constant error due to poor data feeds.

4.    Simplified Decision Making – Avoid “decision fatigue” that can be the downfall of a business. It is important for the BI tool dashboard to allow a user to ease of access to even high-end data processing so that warranted decisions can be taken without a glitch.

5.    Time & Money Saver – Lastly, the tool should not be a time or money consumer, because otherwise, businesses tend to take a negative approach towards the BI tool.

Bernard Marr, author of ― Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, stated that “As business leaders, we need to understand that lack of data is not the issue. Most businesses have more than enough data to use constructively; we just don’t know how to use it. The reality is that most businesses are already data rich, but insight poor.”  The scope of self-service BI tools is to try and cut through the precise data by having department specific users navigate through the abundant data and use it to their leverage instead of the traditional group of BI experts salvaging random data.

In nutshell, self-serving BI tools anticipate providing independent access to critical data without any constraints. Nevertheless, it is important to factor in that every tool needs regular maintenance and appraising without which an error-free analytics tool would completely collapse over time.

5 Key Questions to Ask When Evaluating BI Tools

Geoffrey Moore, an American organizational theorist, management consultant, has rightly said: “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” While the market has a growing variety of vendors for BI tools due to technological advancement, every tool is distinct in its own way. The overall picture may be comprehensive and a flashy demo may convince you that it is the right one, but to pick wisely businesses have to remember that the subtle differences between the tools are the main markers to be carefully considered.

To evaluate these differences, one has to first understand the business requirement and question the intricate aspects to get a better insight into the tool’s hidden limitations. Any tool that you pick should primarily be able to organize the surplus data and aptly generate business charted analysis or reports. There is no one model that can be used like a “super brain” to comprehend the business requirements and generate data on its own will.

Some of the basic questions to address before zeroing would be:

1)    Is the tool shallow in collating data?

Allow the business requirements to predetermine the tool’s function because a tool is an aid to the business and not the other way round. As most BI tools just look at the individual organizational silos and tend to miss out on collaborating coherent information, an ideal BI tool should collate information from the entire business operation, analyze the core functional areas, compare data from multiple types of ERP and external data sources, and then generate a consolidated analytical model that involves all the intricacies.

2)    Does the tool generate a mere report or engage analysis? 

Businesses, most often, fail to see the difference between a summary report and an analytical report. The generated report should be able to map through all the different sectors and at the same time, the collated information should not be just a data mesh. Well, sorted data generation should be the key component for proper functioning in a BI tool.

3)    Is the data current or time-stale? 

The tools used in any form of business should generate data that is updated to the current numbers. If this is absent, then the data generated would be just figures of the past rather than the present. The tool should be well equipped to spontaneously downsize essential data in order to ensure that the business stays in the competition and does not get backlogged.

4)    How fast is the tool and how flexible it is? 

An effective BI tool which can turn out spontaneous reports with the collated information is a major advantage for proper projection of growth and damage control in an organization. Any tool that takes days to churn out information will be of no use to the business. Besides, just like technological evolution, the tool should be technically adaptable to the market trends as tools become easily outdated within few months at times.

5)    How soon can the BI tool be put into play and is it an all in one package?

While most organizations pick ready to use services, building custom BI tools should be quick too; as otherwise, the business would be overlaying progress without the requisite projections. Also, there is no one tool that fits the ideal package. Hence, businesses can try out trial runs and then pick one that fits their parameters and tweak the little details through their IT department in order to ensure that their needle in the haystack is sorted out.

Have you evaluated your BI tool on the above criteria? What are some primary factors you consider while evaluating Business Intelligence tools & services? Sigma’s BI services ensure flexibility on BI tools which are suited best for your business. Do leave your thoughts in the comments section below.

AI in BI – Intelligence,The Way Forward

Traditionally, business intelligence (BI) was restricted to business analysts who supplied information based on a collection of data over set time periods. The evolution of data and the collection of real-time data has greatly influenced the structuring of BI trends. The speed of data is imperative to drive timely actionable insights. Data that had been the metrics a day back would become stale within the next few days. Consequently, live access to data and its immediate interpretation has become the core of BI models. Like data, BI models too have started changing constantly with time bridging the time gap between data gathering and analysis.

The dawn of digitalization

The metrics of digitalization, consumerization, agility, security, analytics, cloud, and mobile are also simultaneously influencing the changing landscape of BI. One of the revolutionizing ideas that are taking form for better BI control is Artificial Intelligence, the AI. This has become a new face in the BI space as real-time data crunching has become more demanding for second by second analysis. Using the evolution of built in algorithms and age-old data analysis tools, businesses could build effective models through AI. It makes the data not just live but also visualized for effective analysis.

Current tech landscape of AI

The purpose of business analytics is to answer and project what the future holds. Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) are two common techniques that are enabling better BI under predictive analysis. While the ANN models work just like the neurons of the human body in trying to chain the data into visualization, the latter technique, ARIMA, is concentrated on time series analysis that predicts scenarios by synchronizing both the past and current data.

Besides providing real time data analysis tools, Artificial Intelligence (AI) is indeed engulfing the business intelligence. We have witnessed several business modules incorporating AI models for efficient functioning and success of their business models. It would be safe to say that some of the areas where AI has been quite successful would be in sales, general electric companies that deal with intricate repairing of machinery, hospitals and in certain cases to monitor machine fleets and factories. If we are not convinced with the fact that AI is slowly taking over BI – here is a fact for you – AI is now the new decision maker! If you are looking for a smart business partner, you know who to reach out to next! This brings a rather intriguing quote from Woodrow Wilson, who has stated: “We should not only use the brains we have but all that we can borrow.” Ain’t that quaint?

The way forward

We have seen upscale in BI technologies such as cloud analytics and embedded integration systems all through the year 2016 and they will continue to reign the BI world since smaller businesses are still in the process of shifting gears into bigger technology. The year 2017 has been predicted by business analysts as the year for businesses to start migrating into the technologically advanced BI models.

Here is a million dollar question – Are you leveraging BI to your strength?

Drop a comment, and I’d be happy to discuss the future of BI with you.