Data to Decisions

Integration, Automation, and Data Analytics

Flow - A Comparison to Custom Code Solutions

Efficiency Flow delivers efficiency by automating the exchange of data between applications and sys

Efficiency

Flow delivers efficiency by automating the exchange of data between applications and systems that “don’t talk to each other.” Flow provides cloud integration solutions across industries for CRM, ERP, databases, and other critical systems. Their solutions are powered Flow, a proprietary software application, which overcomes complex data integration challenges quickly and provides long-term flexibility to grow with a business.

Speed to Deliver 

Flow has built-in interfaces to common data sources like databases, file formats, and online services. When new interfaces must be implemented, the internal structure and layout of Flow provides a best-practices framework for coding them rapidly and accurately. Coupling these interfaces with a flexible, schema-less internal data structure and powerful mathematical and set-based manipulation functions allow complex integration challenges to be solved quickly.

Compared to custom code solutions, Flow can help your business realize shorter time-to-value intervals and accelerate go-live times.

Changeability

Solutions built using Flow fit into a long-term approach to solving integration challenges. Modifications to existing integrations can be made directly using the Workflow Editor, without relying on involvement by Flow. Custom code solutions rely on the availability of the vendor to perform modifications, and the hard-coded nature of custom solutions does not easily accommodate frequent changes to the integration process.

Unlike custom-code, solutions built using Flow are designed to provide long-term flexibility to grow and change with your business.

Cost-Effective

Solutions built using Flow require a minimum of custom software code. Most steps in the integration workflow rely on the “out-of-the-box” library of common functions and interfaces built in to Flow. This reduces the typical bulk of a custom-code software engagement and allows Flow to deliver integration solutions with lower initial setup fees.
By leveraging the power of Flow to build out integration solutions, Flow can reduce the need for expensive, time-consuming custom coding.

Scalability

Flow is delivered under a SaaS model, with a central management Portal controlling multiple, local Agents. Creating new application integrations is as simple as deploying new workflows to existing Agents. Additional Agents can be added on the fly to integrate additional hardware environments or to distribute load.

In contrast, custom code is frequently brittle, and it does not scale well without extensive code changes. Typically, custom code solutions are built only to the original specification, and are not coded with scalability in mind.

Centralized Management

Integrations built using Flow can be deployed to one or more Agents running inside your enterprise. Regardless of where the integrations are running, they can all be managed centrally from the Flow Portal.

Unlike managing many disparate, custom code solutions, Flow enables a long-term strategy by serving as the integration hub for your entire organization.

Minimal IT Resources

Flow can be managed and configured using the Flow Portal, accessible via any web browser. Installation of the local Agents is also easy, requiring minimal hardware resources and IT involvement. The Agents are designed to require no direct interaction, with all control and monitoring performed via the Portal. In addition, updates to the Agents are performed automatically by Flow and don’t require IT staff to oversee the process.

Flow for IT & Data Services

IT and Data Services: New Challenges Every Day In the past the word "data" was rarely uttered outsid

IT and Data Services: New Challenges Every Day

In the past the word "data" was rarely uttered outside the IT department. This has changed. Data is now a strategic differentiator for most organizations and demand for it is increasing rapidly. Not only are IT and data service professionals required to share, convert, and integrate data across internal application portfolio, they must increasingly integrate and deliver information from external sources as well. The techniques and technologies developed over the past two decades were not designed to solve these challenges.

Flow Moves Beyond Current Best Practice Architectures

Flow improves upon the techniques and technologies developed for data integration and business analytics over the past two decades. It employs an advanced data virtualization layer that operates across internal and external systems and locations Flow also provides a real time integrated view of data to data consumers across locations and systems in the required format.

How Flow Helps IT and Data Services in with the Advent of Big Data

  • Combine unstructured and traditional structured sources of data
  • Access data sources from the public cloud to the local departmental and individual files
  • Transform data into a common view
  • Process data more efficiently than data warehousing and incorporate new types of data faster than traditional ETL tools
  • Include a broader set of data types and sources
  • Leverage existing technology investments
  • Scale incrementally

Advanced Data Virtualization

Flow Workflows automate integration processes. Work flows simplify processes by eliminating the need to acquire, design, or build intermediate systems to store and transform data. Using work flows, disparate data can be accessed, joined, grouped, and summarized in-memory. This means solutions can be ready and insight delivered in days not months.

Cloud workflow execution and orchestration automates end-to-end business processes

Flow Connects to disparate data sources including:

  • SaaS applications
  • Business productivity applications
  • Cloud data stores
  • On-premise
  • Relational DBMS
  • Structured and flat file
  • Unstructured data

Integrated Robust and Flexible
Flow dashboards and reports offer an integrated, robust and flexible presentation layer with a full suite of analytics capabilities, including statistics, data and text mining, forecasting, and optimization – all integrated within the business context for better decision making.

Customer Data Integration Leads to Improved Customer Intelligence

Banks, insurance and securities firms, lenders, and other financial institutions struggle with lever

Banks, insurance and securities firms, lenders, and other financial institutions struggle with leveraging existing systems that do not share information with each other very well. This problem only worsens when business processes change and new systems are added to meet evolving requirements. A key challenge of integrating these diversified and distributed systems into a coherent, efficient infrastructure is creating a comprehensive view of the customer.

A comprehensive view of the customer answers critical business questions such as:

  • What are the most profitable opportunities for growth?
  • Which marketing actions will have the greatest impact?
  • What activities will have the maximum impact across lines of business?

Customer data integration solutions for financial services deliver a unified view of data across an entire organization, from both internal and external sources, maximizing the value of customer information. These solutions streamline business processes, promote efficiency, meet compliance demands, strengthen relationships with customers, and reduce operational costs.

New Sources of Customer Data

The Financial Services industry is in the midst of revolutionary new technology trends with the emergence of social, mobile, and cloud computing. These emerging trends require businesses to innovate and bridge the gap between new technologies that offer opportunities for growth and the maintenance of legacy system. Financial Services organizations that bridge this gap will be able to capitalize on these changes through more effective customer intelligence and position themselves for future success, enabling them to gain a competitive advantage.

Customer data integration solutions empower Financial Services organizations to proactively engage customers using social media for product development and innovation, sales and marketing activities, public relations campaigns, and customer service initiatives. Financial Services organizations can gain a competitive edge by tapping in to these new social media channels.

Mobile Devices

Customer interaction via mobile devices like smartphones and tablets plays an increasingly important new role as more consumers are turning to the convenience of these devices for their financial service needs. Financial Services organizations that are taking advantage of the ubiquitous nature of mobile devices have access to an important channel to reach and engage customers at home, work, or on-the-go. Mobile devices streamline and simplify access, allowing customers to quickly connect to a variety of financial services.

Customer data integration solutions enable Financial Services organizations to get right information into the hands of the customer by bringing data together from different applications and systems. Financial Services organizations can leverage mobile devices as extensions of existing financial services delivery.

Cloud Computing

Cloud computing is fast evolving into a commercially viable alternative for businesses seeking additional value in technology. Financial Services organizations that are abandoning their servers in favor of the cloud are reducing IT costs and freeing up more time for customer service. Cloud computing offers the benefits of scalability, flexibility, accessibility, and fast implementation.

Customer data integration solutions allow Financial Services organizations to reap the benefits of cloud computing by integrating cloud services with legacy infrastructure. The cloud naturally supports mobility, enabling internal resources to access email, software, and databases remotely. Financial Services organizations can utilize cloud computing to increase productivity and customer focus without negatively impacting the bottom line.

Leveraging New Sources of Customer Data

Attempts to leverage customer information for increased profitability have relied upon integration of customer data from a mix of operational systems. These systems were designed to support specific business functions, not to work together seamlessly. While the objective is to increase profitability through better customer intelligence, the reliance upon operational systems has often had the opposite effect. Dispersed, disconnected operational systems typically require manual integration processes which are complex and inefficient, frustrating attempts to leverage customer information for increased profitability.

Customer data integration solutions support strategy and planning by improving four critical customer intelligence functions:

  • Customer analysis
  • Customer interaction,
  • Customer feedback, and
  • Customer relationship management

Customer data integration enables Financial Services organizations to successfully increase their return on customer relationships by creating a comprehensive picture of the customer with current information from all internal and external sources customer information. Customer data integration solutions can provide access to an integrated, 360-degree view of the customer allowing companies to integrate customer intelligence into their evolving business strategy and planning.

4D-IQ's Flow Data Automation Framework Continues to Grow

4D-IQ's Flow Data Automation Framework Flow is a data automation framework which allows for the rapi

4D-IQ's Flow Data Automation Framework

Flow is a data automation framework which allows for the rapid design and delivery of solutions that can synchronize, consolidate, transform and analyze data across any number of disconnected systems.

Example Sources Include:

  • PostGre
  •  SQL Server
  •  DB2
  • Oracle
  • MS Access
  • SalesForce
  • Redtail
  • PipeDrive
  • Google Apps
  • LinkedIn
  • Twitter
  • Facebook
  • Zoho CRM
  • Flat Files
  • Sharepoint
  • Excel
  • JSON
  • RSS Feeds
  • Lotus Notes
  • Microsoft Outlook

The core of the framework is a new type of database called a generic database. This core allows Flow to connect and communicate with any data source or structure and draw the data into generic data tables. 

Flow’s generic data is bidirectional allowing data to be loaded in from any system and then restructured and output to the same system or any other required format. This allows for systems that can’t normally communicate to pass data and instructions back and forth.

Flow provides an advanced development environment against the generic data to facilitate the rapid design and delivery of reusable and automated workflows. The development environment is used to automate data tasks and can be construct a solution for virtually any data science or integration challenge irrespective of scale or complexity.

Examples of the types of workflows that can be developed include procedures which:

  • Create autonomous data communication streams across systems
  • Implement standards in data across disconnected systems
  • Link and relate data across disconnected systems
  • Create automated cleansing logic
  • Real time monitor data systems for anomalies
  • Trigger conditional based notifications
  • Migrate and unify legacy systems
  • Evaluate advanced conditions in data
  • Create high powered hypercubes
  • Scrub and standardize address data
  • Validate emails and customer names
  • Standardize and clean business entities
  • Automate reporting tasks across many systems
  • Train advanced machine learning and predictive models
  • Perform any type of statistical analysis
  • Reconstruct and transform datasets to create new features
  • Validate and lookup city, state and zip fields against UPS database
  • Perform fuzzy match ups and fuzzy joins
  • Identify and extract duplicates
  • Deliver reports and streamline dashboard generation
  • Perform semantic analysis and semantic matching
  • Implement data dictionaries
  • Execute FTP data transfers
  • Find hidden anomalies and patterns
  •  Implement genetic algorithms
  •  Perform optimized search across n systems

The above are just a few examples of the types of workflows that can be developed in Flow. The Flow development environment is modeled after a high level programming language and is interacted with through a configurable interface to eliminate the need to code. Because of this, the scope of solutions that can be developed is incredibly diverse. Almost any procedure that can be developed in languages such as R, Python or SQL can be developed faster and with more power in the Flow environment.

Flow allows for the consolidation, cleansing and analysis of an enterprises data in its entirety. Flow has a built in parallel processing architecture for performing distributed computations allowing it to execute the developed workflows against data sets of any size or scale.

Resource intensive data tasks no matter the complexity can be modeled and eliminated using Flow.

Flow is available free at https://flow-app.com

Three Common Types of Data-Driven Decisions

Strategic, Tactical, and Operational Decisions In my experience, most business decisions are, at lea

Strategic, Tactical, and Operational Decisions

In my experience, most business decisions are, at least to some extent, data-driven decisions. This post takes a quick look at three types of business decisions that are improved and facilitated using a data-driven approach.The three types of business decisions are:

 

Business Analytics - Three Types of Data Driven Business Decisions

 

Strategic Decisions

Strategic decisions determine the long-term direction of a business or organization. Strategic decisions are more difficult to model and structure than tactical or operational decisions. Having said this, there are a number of high-level models, or frameworks, for strategic decisions (such as the Porter five forces model). It is certainly safe to say that most strategic decisions are the least structured of three considered here. One clear example of a strategic decision is the board and executive management of hospital evaluating whether or not to expend capital for expansion of their geriatric facilities over a ten year period - where the goal of this decision is to meet projected increases in demand from a growing population of elderly patients.

Tactical Decisions

These are medium-term decisions (generally three to twelve months) that in many cases involve implementation of one or more strategic decisions. Tactical decisions also involve responding to unforeseen events. Examples of tactical decisions may include fiscal year budget adjustments, re-allocation of marketing resources, or short-term changes in product pricing. A specific example may involve a business' supplier that unexpectedly raises prices requiring the business to consider alternatives - such as a temporary switch to an alternate supplier.

Operational Decisions

Operational decisions are semi-structured, short-term (i.e. generally less than three months) decisions generally involving tasks required to implement tactical decisions. An example may be the decision to increase near-term staffing levels in an anticipation of increased short-term demand for products and services.

Additional Decision Types

This blog is about data-driven decision making, business analysis, data management, and automation. These following two classes of business decisions are well covered elsewhere but still warrant a mention here. They are:

  • Programmable decisions - these are decisions with a single fixed goal that require following a set of steps or prescribed procedure. These decisions usually follow a predefined set of steps or written, formal procedures that prescribe certain actions.
  • One-time, non-programmable decisions. These are decisions that arise from one-time, non-routine events. Each decision has a highly unique goal and requires a one-time evaluation of a set of alternatives that are unlikely to reoccur.

The Six Steps of Data-Driven Decision Making

Fundamental Overview of the Data-Driven Decision Making Process This post provides a high-level over

Fundamental Overview of the Data-Driven Decision Making Process

This post provides a high-level overview of the data-driven decision making process broken down into six steps. Successful adoption of data-driven decision making requires a general understanding of each of these steps, the processes involved, and how they relate to each other. The data-decision making process is fundamentally the same regardless of:

  • The type of business decision
  • The size of the business - from small to large
  • The number of decision makers - from one to many

In reality, it is impossible to carry out the six steps of data-driven decision making without the aid of technology. However, the process is not dependent upon a particular set of technologies set. The purpose of this post is to aid understanding of the fundamentals which will later aid the evaluation of particular technologies.

The Six Steps

If the culture of a business does not support the data-driven decision making process, no amount of investment in technology or consulting will confer any competitive advantage. Likewise, decision makers, armed with sophisticated decision tools, who still employ a ready shoot aim approach to data collection for data-driven decisions, have wasted their companies' time and money. It is therefore important for executives and managers to understand both the individual steps in the process of data-driven decision making as well as the entire process. The following provides a high level overview of the data-driven decision making steps.

Data-Driven Decision Making Steps

Business Analytics - The Data-Driven Decision Process

Establish a Goal (Step One)

The single most important step in the data-driven decision process is establishment of a goal. Failure to define a specific goal is like starting a journey with no destination. You may see some interesting sights but who knows where you'll end up. A well-defined goal has two important attributes: it is both attainable and measurable. It almost goes without saying that no decision process will deliver results against an unattainable goal. Likewise, failure to precisely quantify what constitutes attainment of a goal is equally as bad. In this case, a business may seem to have attained a goal when in fact they have not.

Define and Model Alternatives (Step Two)

Establishing a goal is about where to go. Modeling alternatives is about how to get there. There are two primary steps involved: first, quickly eliminate the majority of unfeasible alternatives and, second, develop a short list of feasible alternatives. A quantitative model of each alternative in the final list must then be developed.

Identify Required Data (Step Three)

Identify the data required by each alternative and the metrics associated with the alternative's model. Here, the quality, accuracy, and timeliness of the data must be considered. If the required data is low quality, inaccurate, or out-of-date, the alternative should be removed from the list.

Collect and Organize Data (Step Four)

It should be no surprise, that the data-driven decision making process depends on rapid access to timely and accurate data. Nor should it be a surprise that data management considerations play a central part in the data-driven decision making process. Without efficient and fast data management processes in place collect and organize supporting data, a data-driven decision making process is nearly impossible to implement. It is therefore imperative to build data-driven decision making processes around sound data management processes.

Data Analysis (Step Five)

The relationship between goals, alternatives, metrics, and data forms the data-driven decision making. Well defined goals, alternatives and metrics should make this nothing more than a technical step. However, business must have decision-makers, analysts, or other resources that understand how to apply data analysis techniques within the context of a data-driven decision process.

Decide and Execute (Step Six)

Ultimately, someone has to decide and often others must execute. While making decisions and executing them are a step in the data-driven decision process, this last step is also part of a varying number of other business processes. However, including execution as a step is necessary because (quite obviously) there is no value to making data-driven decisions if they are never executed. As a final note, the results of a decision and its execution provide feedback to the establishment of new goals thereby forming a continuous loop.

Business Analytics - The Role of Metrics

Metrics and the Data-Driven Decision Making Process This post discusses the role of metrics and thei

Metrics and the Data-Driven Decision Making Process

This post discusses the role of metrics and their evaluation within the overall context of the six steps for making data-driven decisions. Reaching an established goal starts with defining a set alternatives that will lead you there. Metrics are established to evaluate alternatives and their suitability for reaching a goal. It is the analysis and evaluation of metrics that turns a decision process into a data-driven decision process. The goal is to optimize decisions through improving the quality of metrics and improving their ongoing evaluation. This is depicted below as a linear relationship between the precision of analytic models and the quality of data-driven decisions.

Improving the Quality of Metrics

A collection of metrics forms a model. This model is used in each step of the data-driven decision process. It follows that improving the quality and precision of a model (as ultimately measured by outcomes) will improve the quality of data-driven decisions. Most common business problems do not require advanced analytical techniques. There are, however, a small set of relatively simple, commonly applied analytical techniques that can be used to improve the precision and application of models. These techniques generally fall within two broad areas of basic descriptive statistics: numerical and visual.

Three Widely Applied Numerical Techniques

There are three widely applied numerical techniques employed by business analytics. They are:

  • Data location (or measures of central tendency) - these techniques measure or summarize the point of central value within a given data set. Example techniques include mean, median, and mode.
  • Variability or dispersion - these techniques measure the amount of scatter or distance of all individual data points from their central location. Examples techniques include variance and average deviation
  • Identification of outliers - this technique refers to the identification of data point values that lie far outside the average dispersion of all other data points

Three Widely Applied Visualization Techniques

Data visualization, or charting, techniques provide a way to visualize data location, variability, and outliers. Three of the most widely used data visualization techniques are:

  • Histogram - a graphical representation of the variability or dispersion of data within a set
  • Frequency distributions - a graphical representation that plots how many times each unique data point value in a data set appears
  • Plot (box, scatter, bar, diagnostic. and others) - graph that plots the value of each data point against two independent variables located on the x and y axis of a chart.

Use Comparison to Analyze Patterns and Differences

By analyzing the patterns and differences in the sample data, generalizations can be formed regarding the relationships or associations among data sets and data points. This generally involves aggregating (or summarizing) the data along several different dimensions (or measures) then looking for patterns and differences.

Summary

The goal of fact-based decision making is to improve the quality of decisions through the application of precise analytic models. The business analytic techniques used to find initial patterns and differences fall within two areas of descriptive statistics: numerical and visual. Three widely used numerical techniques are data location, variability, and outliers. Three commonly used visualization techniques are histogram, frequency distribution, and plots.

Emerging Technologies Push Business Analytics Towards Tipping Point

The Challenge of Emerging Technologies In my prior post, I discussed the weaknesses of the current b

The Challenge of Emerging Technologies

In my prior post, I discussed the weaknesses of the current best-practice approach to business analytics. This post expands on that discussion within the context of emerging technologies.

Best-practice architectures have weaknesses relative to the current business environment. However, that environment is rapidly changing. Emerging technologies require business to create processes for increasingly decentralized information sources. Existing architectural weaknesses combined with the demands of emerging technologies are pushing current best-practices architecture toward a tipping point.

The Tipping Point is Near

Competitive Advantages Realized by Early Adopters Will Create a Tipping Point

The tipping point will be reached when early adopters implement solutions that overcome the weaknesses of the current best-practices architecture and also address the need to operate in an increasingly decentralized information environment. Those businesses that successfully move beyond this tipping point will be in a superior competitive position.

Move Your Business beyond the Tipping Point

Business must adopt a new architecture to gather, analyze, report, and act on information. This architecture must overcome the limitations imposed by current best-practice architectures. It must be radically simpler, faster, more powerful, and agile than current best-practices architecture to meet this fundamental business need.

What are some of the requirements of this new architecture? Here are a few:

  • Eliminate impediments that prevent business from rapidly gathering data required to drive current, critical decisions.
  • Remove the need for an operational data store through adoption of solutions capable of incremental loading of required data.
  • Real-time, or near real-time, processing and analysis of incrementally acquired data.
  • Eliminate the requirement to store analytical data in intermediate, schema bound data repositories.
  • Take advantage of increased memory and processing power to expand the capabilities of in-memory analytics.
  • Eliminate the time and expense of custom coded middle tier business logic.
  • Perform sophisticated mathematical and statistical in-memory flat and hypercube data sets.
  • Enable business to implement business logic for management of decentralized business processes and workflows.

Evaluation of New Solutions

Any evaluation of solutions to meet these and other requirements must include the explicit recognition of point solutions developed to redress narrow limitations of current best practices architecture. These point solutions often fail to address underlying weaknesses as whole.

 

 

Beyond the Current Best-Practice Approach to Business Analytics

The Approaching Business Analytics Tipping Point The competitive position of business is greatly imp

The Approaching Business Analytics Tipping Point

The competitive position of business is greatly impacted by analytics; that is, the ability to gather, analyze, report, and act on information. The impact of analytics creates competitive forces that drive technological change. Over the past two decades, those forces have driven business adoption of core best-practice architectures that are rapidly approaching a tipping point. Competitive pressure, inherent architectural weaknesses, and newly emerging technologies will combine to compel business to adopt a new architecture. Some points to consider are:

  • Business has the basic need to gather, analyze, report, and act upon information.
  • This need is driven by competitive forces that also drive the adoption and growth of new technologies.
  • Competitive forces drove the evolution of the current best-practices architecture over the past two decades.
  • Nearly all current best-practices architecture follows a similar fixed implementation pattern.
  • Post implementation, the fixed nature of best-practice architectures create increasing problems which accumulate over time to add to size and complexity of the solution.
  • Competitive forces are driving the adoption of emerging technologies that compound the weaknesses of current best-practices architecture.
  • A technology tipping point is rapidly approaching; it will create significant competitive advantages for businesses that strategically move beyond current best-practice architectures.

Analytics - a Basic Business Need

Driven by competitive forces, all businesses have the basic need to gather, analyze, report, and act on information. The fundamentals of this need remain nearly constant. However, competitive forces also drive the adoption of new technologies which change the volume and type of information business must act on. The evolution of technology over the past two decades has resulted in a current set of core best-practice architectures employed to meet this basic need.

Variants of the current core best-practice architectures follow a similar implementation pattern. This pattern usually involves implementation of a solution to:

  • Identify, extract, and store fixed sets of data required for analysis
  • Create and load operational data stores, data marts, or data warehouses to store required data
  • Design business logic to access and process these fixed data sets
  • Develop static or ad-hoc reports and dashboards against these data sets

Weaknesses of the Current Best-Practices Architecture

 

Cumulative Change Adds Size and Complexity

Post implementation this best-practices architecture must be modified in response to changing business needs. Generally, each modification requires process changes and increases the volume of information. These cumulative changes add to the size and complexity of the implementation and frequent optimizations become required to maintain acceptable response times.

 

Size and Complexity Creates Business Frustration

Over time, frustrations with slower response times spur the creation of ad-hoc applications and local data marts to meet business exigencies. Point solutions have emerged to cope these with these frustrations. These include new software products and modifications to existing ones. However, these point solutions fail to address the underlying weaknesses in current best-practice architecture.

 

The Challenge of Emerging Technologies

Emerging technologies are another reason business must adopt a new analytics architecture. Read More.

 

 

 

Four Key Benefits of Data Integration for Investment Advisory Firms

Data is an Important Resource for Investment Advisory Firms Investment professionals, like so many o

Data is an Important Resource for Investment Advisory Firms

Investment professionals, like so many of us, often find it difficult to tell the difference between ‘information' and 'data'. However, the business impact of new data sources available to the financial services industry make it imperative to understand the difference between these terms. Information, such as client reports, portfolio metrics, and dynamic portfolio recommendations, is the primary product produced by most advisory firms. Further, data, including market, client, and economic, is the raw material used to produce it. Therefore, data is one of the most important resources owned or used by an investment advisory firm.

The level of data integration employed by a firm is a good measure of the efficiency with which a firm produces their service. It may also be a good indicator of an advisory firm's client service, specifically the quality. This makes the level of data integration an important indicator of a firm's current and future health and profitability.

Unfortunately, merely mentioning the words data or integration is often enough to make many financial advisor professionals abruptly refer you to the nearest available IT employee. I am, of course, exaggerating to make a point, which is: investment advisory professionals should pay close attention to discussions of data and integration. Outlined below are four key reasons data integration deserves attention in financial services:

Four Key Reasons Data Integration Benefits Investment Advisory Firms

1. Increased New Business Opportunities

More often the competition for new business is won by firms able to create information that serves client needs as understood from a complete 360° view of a client. Since the competition among firms for assets is a zero-sum game, information provided to clients based upon a complete and accurate view of their needs not only win the day for the advisory service but also positively impacts client retention rates. Firms that have efficient data integration capabilities will be able create and provide a better information service to their customers. That is, they will be able to offer timely, high-quality information that is uniquely tailored to the needs of their current and prospective clients. To generate this information, these firms will need to efficiently integrate data from a large number of heterogeneous sources. These include internal operational data, third-party data, and newer data sources such as social media.

2. Increased Efficiency and Decreased Costs

There are two primary service delivery approaches implemented by investment advisory firms: best-of-breed and end-to-end. Each of these approaches creates unique integration complexities that increase costs - including data duplication, required customization, and process inefficiency.

Data is a perishable commodity - it’s a raw material that possesses a limited shelf life. Therefore, rapid and efficient access to timely data reduces waste. Over time, the logistics of internal data integration have been made more complex due to data duplication arising from siloed best-of-breed applications or inflexible end-to-end solutions. The best-of-breed and end-to-end solution approaches have made internal data integration more difficult and complex. The additions of external data sources, such as social media, add a new layer of complexity on top of this. Therefore, front, middle, and back-office technology don’t easily translate into increased efficiency. More efficient use of existing technology primarily means more efficient data integration.

Data integration remains a complex process, so a “light weight,” low cost, and efficient data integration solution managed with proper skill and expertise are needed to overcome this complexity. Such a solution, implemented as an overlay on existing technology, will greatly decrease costs.

3. Decreased Risk Exposure

Data integration efficiency directly impacts a firm’s risk exposure. Complex integration, outdated data, and poor data quality lead to the production of inconsistent, outdated, or inaccurate information – garbage in, garbage out. This greatly increases risk exposure.

Decreasing risk exposure requires timely and accurate data to be delivered both internally and externally. This enables informed decisions on the part of everyone involved in the delivery process. It also insures that processes are consistent, which results in compliant information provided to clients and regulators.

4. Increased Product Quality

Investment advisory firms’ primary product is information, and the quality of that product is directly impacted by the value and relevance of the underlying data.

Increased product quality requires advisory firms to have timely and efficient access to the most up-to-date and accurate data. In addition, product quality is increasingly defined by how well information provided to clients reflects their unique personal circumstances. This requires integration of data that is unique and customer centric. Data integration flexibility, therefore, is becoming extremely important

Data integration has a profound impact upon the quality of product produced by investment advisory firms. Moreover, product quality will increasingly be measured by how well a firm delivers information that is tailored to unique client needs. As clients begin to expect information tailored directly to their needs (think Amazon), efficient, high value data integration services will become more of a business critical function for investment firms.